Reviewerly/exHarmony huggingface dataset:
A Temporal and Social Network-based Recommender using Graph Clustering

Recommendation Systems (RSs) have significant applications in many industrial systems. The duty of a recommender algorithm is to operate available data (users/items contextual data and rating (or purchase) the consumption history for items), as well as to provide a recommendation list for any target user. The recommended items should be selected so that the target user is compelled to give them positive reviews. In this manuscript, we propose a novel of RS algorithm that makes advantage of user-user trust relationships, rating histories, and their frequency of occurrence. We also provide a brand new overlapping community detection algorithm. The information about the users’ community structure is used to handle the cold-start and sparsity problems. We compare the performance of the proposed RS algorithm with a number of state-of-the-art algorithms on the extended Epinions dataset, which has both information on trust relations and the timing of the ratings. Numerical simulations reveal the superiority of the proposed algorithm over others. We also investigate how the algorithms perform when only cold-start users and items are considered. As a cold-start user (item) we consider those that have made (received) less than five ratings. The experiments show significant outperformance of the proposed algorithm over others, which is mainly due to the use of information on overlapping community structures between users.
The Determinants of Digital Workplace Adoption: A Conceptual Framework

The digital workplace is an invaluable organizational resource that can significantly boost the productivity of knowledge workers.The reduction of barriers between people, information, and processes is one of the many benefits offered digital workplace, along with mobility, information searching, communication, and collaboration.However, the Movement Control Order caused commercial losses for more than two thirds of Malaysian SMEs (MCO).31% of SMEs suffered delays in finishing tasks and projects, while 30% had operational interruptions.From a thorough analysis of the literature on digital workplace adoption adoption, this study presents a conceptual framework to determine the factors that influence digital workplace adoption.The conceptual framework which is based on Unified Theory of Technology Acceptance and Use (UTAUT) will be applied in this study.The proposed framework can be utilized to identify the determinant factors of digital workplace adoption among employees of Malaysian SMEs.In the meantime, Personal Knowledge Management is integrated to the framework serves as a moderator to assess worker's ability in using digital workplace tools.
Efficient approximate unitary t-designs from partially invertible universal sets and their application to quantum speedup

At its core a $t$-design is a method for sampling from a set of unitaries in a way which mimics sampling randomly from the Haar measure on the unitary group, with applications across quantum information processing and physics. We construct new families of quantum circuits on $n$-qubits giving rise to $\varepsilon$-approximate unitary $t$-designs efficiently in $O(n^3t^{12})$ depth. These quantum circuits are based on a relaxation of technical requirements in previous constructions. In particular, the construction of circuits which give efficient approximate $t$-designs by Brandao, Harrow, and Horodecki (F.G.S.L Brandao, A.W Harrow, and M. Horodecki, Commun. Math. Phys. (2016).) required choosing gates from ensembles which contained inverses for all elements, and that the entries of the unitaries are algebraic. We reduce these requirements, to sets that contain elements without inverses in the set, and non-algebraic entries, which we dub partially invertible universal sets. We then adapt this circuit construction to the framework of measurement based quantum computation(MBQC) and give new explicit examples of $n$-qubit graph states with fixed assignments of measurements (graph gadgets) giving rise to unitary $t$-designs based on partially invertible universal sets, in a natural way. We further show that these graph gadgets demonstrate a quantum speedup, up to standard complexity theoretic conjectures. We provide numerical and analytical evidence that almost any assignment of fixed measurement angles on an $n$-qubit cluster state give efficient $t$-designs and demonstrate a quantum speedup.
Efficient Sequence Packing without Cross-contamination: Accelerating Large Language Models without Impacting Performance

Effective training of today's large language models (LLMs) depends on large batches and long sequences for throughput and accuracy. To handle variable-length sequences on hardware accelerators, it is common practice to introduce padding tokens, so that all sequences in a batch have the same length. We show in this paper that the variation in sequence lengths in common NLP datasets is such that up to 50% of all tokens can be padding. In less common, but not extreme, cases (e.g. GLUE-cola with sequence length 128), the ratio is up to 89%. Existing methods to address the resulting inefficiency are complicated by the need to avoid cross-contamination in self-attention, by a reduction in accuracy when sequence ordering information is lost, or by customized kernel implementations only valid for specific accelerators. This paper introduces a new formalization of sequence packing in the context of the well-studied bin packing problem, and presents new algorithms based on this formulation which, for example, confer a 2x speedup for phase 2 pre-training in BERT. We show how existing models can be adapted to ensure mathematical equivalence between the original and packed models, meaning that packed models can be trained with existing pre-training and fine-tuning practices.
Rolling Shutter Inversion: Bring Rolling Shutter Images to High Framerate Global Shutter Video

A single rolling-shutter (RS) image may be viewed as a row-wise combination of a sequence of global-shutter (GS) images captured by a (virtual) moving GS camera within the exposure duration. Although RS cameras are widely used, the RS effect causes obvious image distortion especially in the presence of fast camera motion, hindering downstream computer vision tasks. In this paper, we propose to invert the RS image capture mechanism, i.e., recovering a continuous high framerate GS video from two time-consecutive RS frames. We call this task the RS temporal super-resolution (RSSR) problem. The RSSR is a very challenging task, and to our knowledge, no practical solution exists to date. This paper presents a novel deep-learning based solution. By leveraging the multi-view geometry relationship of the RS imaging process, our learning-based framework successfully achieves high framerate GS generation. Specifically, three novel contributions can be identified: (i) novel formulations for bidirectional RS undistortion flows under constant velocity as well as constant acceleration motion model. (ii) a simple linear scaling operation, which bridges the RS undistortion flow and regular optical flow. (iii) a new mutual conversion scheme between varying RS undistortion flows that correspond to different scanlines. Our method also exploits the underlying spatial-temporal geometric relationships within a deep learning framework, where no additional supervision is required beyond the necessary middle-scanline GS image. Building upon these contributions, we represent the very first rolling-shutter temporal super-resolution deep-network that is able to recover high framerate GS videos from just two RS frames. Extensive experimental results on both synthetic and real data show that our proposed method can produce high-quality GS image sequences with rich details, outperforming the state-of-the-art methods.
Uncovering the Structural Fairness in Graph Contrastive Learning

Recent studies show that graph convolutional network (GCN) often performs worse for low-degree nodes, exhibiting the so-called structural unfairness for graphs with long-tailed degree distributions prevalent in the real world. Graph contrastive learning (GCL), which marries the power of GCN and contrastive learning, has emerged as a promising self-supervised approach for learning node representations. How does GCL behave in terms of structural fairness? Surprisingly, we find that representations obtained by GCL methods are already fairer to degree bias than those learned by GCN. We theoretically show that this fairness stems from intra-community concentration and inter-community scatter properties of GCL, resulting in a much clear community structure to drive low-degree nodes away from the community boundary. Based on our theoretical analysis, we further devise a novel graph augmentation method, called GRAph contrastive learning for DEgree bias (GRADE), which applies different strategies to low- and high-degree nodes. Extensive experiments on various benchmarks and evaluation protocols validate the effectiveness of the proposed method.
Improving quantum genetic optimization through granular computing

Abstract Quantum computers promise to revolutionize the world of computing thanks to some features of quantum mechanics that can enable massive parallelism in computation. This benefit may be particularly relevant in the design of evolutionary algorithms, where the quantum paradigm could support the exploration of multiple regions of the search space in a concurrent way. Although some efforts in this research field are ongoing, the potential of quantum computing is not yet fully expressed due to the limited number of qubits of current quantum processors. This limitation is even more acute when one wants to deal with continuous optimization problems, where the search space is potentially infinite. The goal of this paper is to address this limitation by introducing a hybrid and granular approach to quantum algorithm design, specifically designed for genetic optimization. This approach is defined as hybrid, because it uses a digital computer to evaluate fitness functions, and a quantum processor to evolve the genetic population; moreover, it uses granular computing to hierarchically reduce the size of the search space of a problem, so that good near-optimal solutions can be identified even on small quantum computers. As shown in the experiments, where IBM Q family processors are used, the usage of a granular computation scheme statistically enhances the performance of the state-of-the-art evolutionary algorithm implemented on quantum computers, when it is run to optimize well-known benchmark continuous functions.
Effect of the STAD Type Cooperative Learning Model with the Help of Crossword Worksheet on Biology Learning Outcomes, especially the Cognitive Domain

This study aims to determine the effect of the STAD-type cooperative model with the help of Crossword Puzzles on students' biology learning outcomes, especially on Development and Growth material in terms of Cognitive aspects. The population in this study amounted to 5 classes, and a sample of 2 classes was taken randomly. The study was conducted in two classes which were grouped as experimental class and control class. In the experimental class, learning is carried out using a STAD-type cooperative model with the help of crossword puzzle worksheets, while in the control class, learning is carried out using a conventional model research data obtained from the test of learning outcomes for cognitive aspects. Student learning outcomes on cognitive aspects were analyzed by t-test. The results of data analysis show that the learning outcomes of cognitive domain biology students whose learning uses the STAD-type cooperative model with the help of crossword puzzle worksheets are higher than students whose learning uses the conventional model.
Students’ Anxiety in Speaking Class: An Investigation in Online and Offline Learning System

This study aims to investigate English Education students speaking anxiety in both online and offline learning contexts. This study did a further investigation about the strategies they use to cope with their anxiety when doing public speaking in both online and offline contexts. Although there is a lot of research regarding speaking anxiety has been done by the researchers both in Indonesia and other countries, there are still limited studies discussing speaking anxiety in education especially in both offline and online learning among English education students in Indonesia. This study employed a qualitative method. The data were collected through interviews with two students from a private university in Indonesia. The findings showed that both participants were less anxious when they do public speaking in the online context than in offline one. Furthermore, it was also found that these participants only used one strategy to overcome their anxiety (i.e., preparation) for online public speaking; and used more strategies (i.e., relaxation, preparation, positive thinking, audience depreciation, concentration, and resignation) to deal with offline speaking anxiety. Therefore, it is implied that through these coping strategies,
GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Dense retrieval approaches can overcome the lexical gap and lead to significantly improved search results. However, they require large amounts of training data which is not available for most domains. As shown in previous work (Thakur et al., 2021b), the performance of dense retrievers severely degrades under a domain shift. This limits the usage of dense retrieval approaches to only a few domains with large training datasets. In this paper, we propose the novel unsupervised domain adaptation method Generative Pseudo Labeling (GPL), which combines a query generator with pseudo labeling from a cross-encoder. On six representative domain-specialized datasets, we find the proposed GPL can outperform an out-of-the-box state-of-the-art dense retrieval approach by up to 9.3 points nDCG@10. GPL requires less (unlabeled) data from the target domain and is more robust in its training than previous methods. We further investigate the role of six recent pre-training methods in the scenario of domain adaptation for retrieval tasks, where only three could yield improved results. The best approach, TSDAE (Wang et al., 2021) can be combined with GPL, yielding another average improvement of 1.4 points nDCG@10 across the six tasks. The code and the models are available at https://github.com/UKPLab/gpl.
A Literature Review Research on Monitoring Conditions of Mechanical Equipment Based on Edge Computing

The motivation of this research is to review all methods used in data compression of collected data in monitoring the condition of equipment based on the framework of edge computing. Since a large amount of signal data is collected when monitoring conditions of mechanical equipment, namely, signals of running machines are continuously transmitted to be crunched, compressed data should be handled effectively. However, this process occupies resources since data transmission requires the allocation of a large capacity. To resolve this problem, this article examines the monitoring conditions of equipment based on edge computing. First, the signal is pre-processed by edge computing, so that the fault characteristics can be identified quickly. Second, signals with difficult-to-identify fault characteristics need to be compressed to save transmission resources. Then, different types of signal data collected in mechanical equipment conditions are compressed by various compression methods and uploaded to the cloud. Finally, the cloud platform, which has powerful processing capability, is processed to improve the volume of the data transmission. By examining and analyzing the monitoring conditions and signal compression methods of mechanical equipment, the future development trend is elaborated to provide references and ideas for the contemporary research of data monitoring and data compression algorithms. Consequently, the manuscript presents different compression methods in detail and clarifies the data compression methods used for the signal compression of equipment based on edge computing.
Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters

To diversify and enrich generated dialogue responses, knowledge-grounded dialogue has been investigated in recent years. The existing methods tackle the knowledge grounding challenge by retrieving the relevant sentences over a large corpus and augmenting the dialogues with explicit extra information. Despite their success, however, the existing works have drawbacks in inference efficiency. This paper proposes KnowExpert, a framework to bypass the explicit retrieval process and inject knowledge into the pre-trained language models with lightweight adapters and adapt to the knowledge-grounded dialogue task. To the best of our knowledge, this is the first attempt to tackle this challenge without retrieval in this task under an open-domain chit-chat scenario. The experimental results show that Knowexpert performs comparably with some retrieval-based baselines while being time-efficient in inference, demonstrating the effectiveness of our proposed method.
Multi-Organ Segmentation Using a Low-Resource Architecture

Since their inception, deep-learning architectures have shown promising results for automatic segmentation. However, despite the technical advances introduced by fully convolutional networks, generative adversarial networks or recurrent neural networks, and their usage in hybrid architectures, automatic segmentation in the medical field is still not used at scale. One main reason is related to data scarcity and quality, which in turn generates a lack of annotated data that hinder the generalization of the models. The second main issue refers to challenges in training deep models. This process uses large amounts of GPU memory (that might exceed current hardware limitations) and requires high training times. In this article, we want to prove that despite these issues, good results can be obtained even when using a lower resource architecture, thus opening the way for more researchers to employ and use deep neural networks. In achieving the multi-organ segmentation, we are employing modern pre-processing techniques, a smart model design and fusion between several models trained on the same dataset. Our architecture is compared against state-of-the-art methods employed in a publicly available challenge and the notable results prove the effectiveness of our method.
Hybrid Fine-Tuning Strategy for Few-Shot Classification

Few-shot classification aims to enable the network to acquire the ability of feature extraction and label prediction for the target categories given a few numbers of labeled samples. Current few-shot classification methods focus on the pretraining stage while fine-tuning by experience or not at all. No fine-tuning or insufficient fine-tuning may get low accuracy for the given tasks, while excessive fine-tuning will lead to poor generalization for unseen samples. To solve the above problems, this study proposes a hybrid fine-tuning strategy (HFT), including a few-shot linear discriminant analysis module (FSLDA) and an adaptive fine-tuning module (AFT). FSLDA constructs the optimal linear classification function under the few-shot conditions to initialize the last fully connected layer parameters, which fully excavates the professional knowledge of the given tasks and guarantees the lower bound of the model accuracy. AFT adopts an adaptive fine-tuning termination rule to obtain the optimal training epochs to prevent the model from overfitting. AFT is also built on FSLDA and outputs the final optimum hybrid fine-tuning strategy for a given sample size and layer frozen policy. We conducted extensive experiments on mini-ImageNet and tiered-ImageNet to prove the effectiveness of our proposed method. It achieves consistent performance improvements compared to existing fine-tuning methods under different sample sizes, layer frozen policies, and few-shot classification frameworks.
Volume Segmantics: A Python Package for Semantic Segmentation of Volumetric Data Using Pre-trained PyTorch Deep Learning Models

Segmentation of 3-dimensional (3D, volumetric) images is a widely used technique that allows interpretation and quantification of experimental data collected using a number of techniques (for example, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Electron Tomography (ET)).Although the idea of semantic segmentation is a relatively simple one, giving each pixel a label that defines what it represents (e.g cranial bone versus brain tissue); due to the subjective and laborious nature of the manual labelling task coupled with the huge size of the data (multi-GB files containing billions of pixels) this process is often a bottleneck in imaging workflows.In recent years, deep learning has brought models capable of fast and accurate interpretation of image data into the toolbox available to scientists.These models are often trained on large image datasets that have been annotated at great expense.In many cases however, scientists working on novel samples and using new imaging techniques do not yet have access to large stores of annotated data.To overcome this issue, simple software tools that allow the scientific community to create segmentation models using relatively small amounts of training data are required.Volume Segmantics is a Python package that provides a command line interface (CLI) as well as an Application Programming Interface (API) for training 2-dimensional (2D) PyTorch (Paszke et al., 2019) deep learning models on small amounts of annotated 3D image data.The package also enables applying these models to new (often much larger) 3D datasets to speed up the process of semantic segmentation.
Implementation of Accountability and Transparency In The Allocation of School Operational Assistance Funds (BOS) During Covid-19.

UPT SD Negeri 191 Bubun Bia is one of the public schools that also received attention in the form of BOS funds from the government. The purpose of this study was to find out how to apply the principles of transparency and accountability in the management of BOS funds during the COVID-19 pandemic at UPT SD Negeri 191 Bubun Bia. This study uses qualitative methods with descriptive analysis and data collection techniques by means of observation, interviews, and documentation. The distribution of BOS Funds is based on the total number of students, totaling 104 students. The population in this study amounted to 218 people with a sample using purposive sampling of as many as 9 people. The results showed that the application of the principle of transparency at the UPT SD Negeri 191 Bubun Bia was transparent, as seen from the openness in the planning of preparing the RKAS. The benefits of this research are to provide mutual trust between various parties and the transparency of financial statements that can be accounted for