Reviewerly/exHarmony huggingface dataset:
Monogamy of quantum discord

The original quantum discord (QD) is shown to be not monogamous except for the three-qubit states. Recently, a complete monogamy relation for multiparty quantum system was established for entanglement in [Phys. Rev. A. {101}, 032301~(2020)], and in addition, a new multipartite generalization of QD was proposed in [Phys. Rev. Lett. {124}, 110401~(2020)]. In this work, we firstly define the complete monogamy for this multipartite quantum discord (MQD) and the global quantum discord (GQD). MQD, with the same spirit as the complete monogamy of entanglement, is said to be completely monogamous (i) if it does not increase under coarsening of subsystems and (ii) if some given combination of subsystems reach the total amount of the correlation, then all other combination of subsystems that do not include all the given subsystems do not contain such a correlation any more. Here, coarsening of subsystems means discarding or combining some subsystems up to the given partition. Simultaneously, the complete monogamy of GQD is also defined with slight modification on the coarsening relation.Consequently, we explore all the coarsening relations of MQD and show that it is completely monogamous with a modicum assumption. In addition, with the same spirit, we investigate all the coarsening relations for GQD and show that GQD is not completely monogamous.That is, in the sense of the complete monogamy relation, MQD as a generalization of the original QD captures the nature of such a quantum correlation, and thus it is nicer than GQD as a generalization.
FedPHP: Federated Personalization with Inherited Private Models

Federated Learning (FL) generates a single global model via collaborating distributed clients without leaking data privacy. However, the statistical heterogeneity of non-iid data across clients poses a fundamental challenge to the model personalization process of each client. Our significant observation is that the newly downloaded global model from the server may perform poorly on local clients, while it could become better after adequate personalization steps. Inspired by this, we advocate that the hard-won personalized model in each communication round should be rationally exploited, while standard FL methods directly overwrite the previous personalized models. Specifically, we propose a novel concept named “inHerited Private Model” (HPM) for each local client as a temporal ensembling of its historical personalized models and exploit it to supervise the personalization process in the next global round. We explore various types of knowledge transfer to facilitate the personalization process. We provide both theoretical analysis and abundant experimental studies to verify the superiorities of our algorithm.
MeSIN: Multilevel selective and interactive network for medication recommendation

Recommending medications for patients using electronic health records (EHRs) is a crucial data mining task for an intelligent healthcare system. EHRs data, which comprise multiple temporal sequences (e.g., lab, diagnosis, and treatment sequences), encompass multilevel structure information, including flat structure information (e.g., sequential medical codes and their relationships) in each sequence and hierarchical structure information between sequences (e.g., information flow from lab sequence to diagnosis sequence or hierarchical codes relationships), which implicitly reflects a physician’s decision process. However, existing studies focus on mining flat structure information, which is incomplete and hence biased. Therefore, to realize more accurate medication recommendations, this paper proposes a multilevel selective and interactive network (MeSIN) that exploits complete structure information of EHR data; in particular, at the core of our framework is an interactive long short-term memory network (InLSTM), which is developed to model the temporal dependencies in each flat-structured sequence via a recurrent mechanism and capture the hierarchical relationships of different sequences via a reinforced interactive network. Moreover, to reduce the impact of irrelevant information on the multilevel interaction process, an attentional selective module (ASM) is introduced to filter out noisy information and thereby focus on important sequence information at each timestamp. Finally, a global selective fusion module (GSFM) is proposed to infuse multisourced information embeddings into multilevel structural relationship-aware patient representation. Experimental results on a practical clinical dataset show that our framework consistently outperformed all baseline models in the medication recommendation task.
Blockchain Technology Transformation in Advancing Future Change

With the development of technology in the 4th century, it can be seen from before the christian period (SM) such as the compass, calendar, pozzolana and wheel. And now the world is about to undergo a tremendous transformation. In this extraordinary change there are inventions such as printing machines, machines, digital computers, vaccines, telegraph technology, ironing tools, airplanes, and even the internet. With this discovery, the world underwent a tremendous transformation from the previous century, due to technological advances over time. Inventions made before Christ must take a long time, even centuries, but making changes today only takes a few years. But in this study, we will describe blockchain technology even centuries, but by making changes now it only takes a few years. But this research will describe blockchain technology. On Facebook, which has a new change to the concept of decentralization, but this concept is still somewhat contradictory to the current world system, so why do users need intermediaries, namely third parties to keep the transaction secure? This research will provide answers to arguments starting with a conceptual understanding which will then go through the stage of case study stages in various industries such as administration, banking, chain management, intellectual management, transportation, even asset management, logistics and energy materials. With blockchain technology, it is used to describe blockchain technology and technology that humans do everyday. There is a concept of a ledger that undergoes a gradual transformation carried out by Satoshi Nakamoto, who has a triple entry ledger that is used to master blockchain technology. In blockchain technology, it has cryptography that is useful for avoiding money laundering, knowing customer activity, knowing all banking transactions and even administration in storing people's identities. In this case study, we will discuss big companies such as IBM, Amazon, Maersk, and even a kind of startup like Ezremit and Signazy. And in this administration section it is important to identify global educational techniques and develop technology in a better direction so that it can advance society in Indonesia and blockchain technology is included in technology that advances future change.
Scalable Hierarchical Agglomerative Clustering

The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability. Existing scalable hierarchical clustering methods sacrifice quality for speed and often lead to over-merging of clusters. In this paper, we present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points. We perform a detailed theoretical analysis, showing that under mild separability conditions our algorithm can not only recover the optimal flat partition but also provide a two-approximation to non-parametric DP-Means objective. This introduces a novel application of hierarchical clustering as an approximation algorithm for the non-parametric clustering objective. We additionally relate our algorithm to the classic hierarchical agglomerative clustering method. We perform extensive empirical experiments in both hierarchical and flat clustering settings and show that our proposed approach achieves state-of-the-art results on publicly available clustering benchmarks. Finally, we demonstrate our method's scalability by applying it to a dataset of 30 billion queries. Human evaluation of the discovered clusters show that our method finds better quality of clusters than the current state-of-the-art.
Augmented Reality with Industrial Process Tomography: To Support Complex Data Analysis in 3D Space

Today, in-situ analyzing and monitoring are imperative for ensuring successful and healthy industrial processes in confined environments. With the rapid development of digitization, augmented reality (AR) has been utilized for letting people immersively interact with the necessary information. However, there are still knowledge gaps between AR technique and domain users pertaining to effective analysis of complex data. Hence, new solutions empowering domain users would benefit the whole industry. In this study, we report an initial prototype supporting complex data visualization and analysis in entire 3D surroundings within industrial process tomography (IPT). Microsoft HoloLens 2 is equipped for users to interact with the 3D information characterizing the workflow of the industrial process with high immersion. Our work distinctly improves the performance compared to existing solutions, pointing the way towards how AR should be deployed and developed more efficiently for aiding IPT systems.
One- and Two-Phase Software Requirement Classification Using Ensemble Deep Learning

Recently, deep learning (DL) has been utilized successfully in different fields, achieving remarkable results. Thus, there is a noticeable focus on DL approaches to automate software engineering (SE) tasks such as maintenance, requirement extraction, and classification. An advanced utilization of DL is the ensemble approach, which aims to reduce error rates and learning time and improve performance. In this research, three ensemble approaches were applied: accuracy as a weight ensemble, mean ensemble, and accuracy per class as a weight ensemble with a combination of four different DL models-long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), a gated recurrent unit (GRU), and a convolutional neural network (CNN)-in order to classify the software requirement (SR) specification, the binary classification of SRs into functional requirement (FRs) or non-functional requirements (NFRs), and the multi-label classification of both FRs and NFRs into further experimental classes. The models were trained and tested on the PROMISE dataset. A one-phase classification system was developed to classify SRs directly into one of the 17 multi-classes of FRs and NFRs. In addition, a two-phase classification system was developed to classify SRs first into FRs or NFRs and to pass the output to the second phase of multi-class classification to 17 classes. The experimental results demonstrated that the proposed classification systems can lead to a competitive classification performance compared to the state-of-the-art methods. The two-phase classification system proved its robustness against the one-phase classification system, as it obtained a 95.7% accuracy in the binary classification phase and a 93.4% accuracy in the second phase of NFR and FR multi-class classification.
Blockchain adoption in supply networks: a social capital perspective

Purpose This study aims to contribute to the early but fervent debate on blockchain and supply networks by proposing a novel theoretical perspective on blockchain adoption grounded on social capital theory. In particular, it seeks to answer the following question: what is the role of social capital in shaping the decision to adopt blockchain in supply networks? Design/methodology/approach Multiple case-studies, based on interviews performed with managers of eight firms, were used. Findings The social capital theory emerged as an additional but necessary lens to investigate blockchain implementation in supply networks. The intuitions proposed highlighted the importance of managers’ sensemaking for investigating technology adoption. Relational capital emerged as a necessary but not sufficient condition to adopt blockchain in supply networks. In addition, it is argued a relationship between competitive opportunities at the firm level and the idea to adopt the blockchain. The opportunity to act as “Tertius Gaudens” or as “Tertius Iungens” information brokers in supply networks should severely affect firms’ proneness toward the adoption of blockchain solutions. Originality/value This is one of the first studies in the literature investigating blockchain adoption in supply networks from a social capital perspective. It introduces new issues to the debate related to the role of blockchain in the supply chain by discussing the role of goal misalignment and competitive advantage, which emerged as crucial for shaping the decision to adopt blockchain in supply networks.
A Risk-Budgeted Portfolio Selection Strategy Using Invasive Weed Optimization

For the investors, portfolio selection from the set of assets in the population is very essential practice in the capital market. Risk minimization is one of the main concerns in portfolio selection problem. Therefore, risk distribution is very common practice in risk-budgeted portfolio optimization. In this paper, a novel risk-budgeted portfolio selection strategy using invasive weed optimization (IWO) is proposed to maximize sharp ratio. Invasive weed optimization is a nature based approach inspired by the invasion process of weeds in the plants. The natural invasion has been mathematically modeled to search the optimum solution in the solution space. An experimental study has been done to evaluate the proposed strategy by comparing its performance with Genetic Algorithm on dataset of the S&P BSE Sensex of Indian stock exchange (30 stocks). Study reveals the superior performance of the proposed strategy.
Chinese Named Entity Recognition Method in History and Culture Field Based on BERT

Abstract With rapid development of the Internet, people have undergone tremendous changes in the way they obtain information. In recent years, knowledge graph is becoming a popular tool for the public to acquire knowledge. For knowledge graph of Chinese history and culture, most researchers adopted traditional named entity recognition methods to extract entity information from unstructured historical text data. However, the traditional named entity recognition method has certain defects, and it is easy to ignore the association between entities. To extract entities from a large amount of historical and cultural information more accurately and efficiently, this paper proposes one named entity recognition model combining Bidirectional Encoder Representations from Transformers and Bidirectional Long Short-Term Memory-Conditional Random Field (BERT-BiLSTM-CRF). First, a BERT pre-trained language model is used to encode a single character to obtain a vector representation corresponding to each character. Then one Bidirectional Long Short-Term Memory (BiLSTM) layer is applied to semantically encode the input text. Finally, the label with the highest probability is output through the Conditional Random Field (CRF) layer to obtain each character’s category. This model uses the Bidirectional Encoder Representations from Transformers (BERT) pre-trained language model to replace the static word vectors trained in the traditional way. In comparison, the BERT pre-trained language model can dynamically generate semantic vectors according to the context of words, which improves the representation ability of word vectors. The experimental results prove that the model proposed in this paper has achieved excellent results in the task of named entity recognition in the field of historical culture. Compared with the existing named entity identification methods, the precision rate, recall rate, and $$F_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> value have been significantly improved.
Algebraic aggregation of random forests: towards explainability and rapid evaluation

Abstract Random Forests are one of the most popular classifiers in machine learning. The larger they are, the more precise the outcome of their predictions. However, this comes at a cost: it is increasingly difficult to understand why a Random Forest made a specific choice, and its running time for classification grows linearly with the size (number of trees). In this paper, we propose a method to aggregate large Random Forests into a single, semantically equivalent decision diagram which has the following two effects: (1) minimal, sufficient explanations for Random Forest-based classifications can be obtained by means of a simple three step reduction, and (2) the running time is radically improved. In fact, our experiments on various popular datasets show speed-ups of several orders of magnitude, while, at the same time, also significantly reducing the size of the required data structure.
Gaze Tracking Using an Unmodified Web Camera and Convolutional Neural Network

Gaze estimation plays a significant role in understating human behavior and in human–computer interaction. Currently, there are many methods accessible for gaze estimation. However, most approaches need additional hardware for data acquisition which adds an extra cost to gaze tracking. The classic gaze tracking approaches usually require systematic prior knowledge or expertise for practical operations. Moreover, they are fundamentally based on the characteristics of the eye region, utilizing infrared light and iris glint to track the gaze point. It requires high-quality images with particular environmental conditions and another light source. Recent studies on appearance-based gaze estimation have demonstrated the capability of neural networks, especially convolutional neural networks (CNN), to decode gaze information present in eye images and achieved significantly simplified gaze estimation. In this paper, a gaze estimation method that utilizes a CNN for gaze estimation that can be applied to various platforms without additional hardware is presented. An easy and fast data collection method is used for collecting face and eyes images from an unmodified desktop camera. The proposed method registered good results; it proves that it is possible to predict the gaze with reasonable accuracy without any additional tools.
Cyber Crime Investigation: Landscape, Challenges, and Future Research Directions

As technology has become pivotal a part of life, it has also become a part of criminal life. Criminals use new technology developments to commit crimes, and investigators must adapt to these changes. Many people have, and will become, victims of cybercrime, making it even more important for investigators to understand current methods used in cyber investigations. The two general categories of cyber investigations are digital forensics and open-source intelligence. Cyber investigations are affecting more than just the investigators. They must determine what tools they need to use based on the information that the tools provide and how effectively the tools and methods work. Tools are any application or device used by investigators, while methods are the process or technique of using a tool. This survey compares the most common methods available to investigators to determine what kind of evidence the methods provide, and which of them are the most effective. To accomplish this, the survey establishes criteria for comparison and conducts an analysis of the tools in both mobile digital forensic and open-source intelligence investigations. We found that there is no single tool or method that can gather all the evidence that investigators require. Many of the tools must be combined to be most effective. However, there are some tools that are more useful than others. Out of all the methods used in mobile digital forensics, logical extraction and hex dumps are the most effective and least likely to cause damage to the data. Among those tools used in open-source intelligence, natural language processing has more applications and uses than any of the other options.
Multitask Prompted Training Enables Zero-Shot Task Generalization

Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning in language models' pretraining (Radford et al., 2019). Can zero-shot generalization instead be directly induced by explicit multitask learning? To test this question at scale, we develop a system for easily mapping any natural language tasks into a human-readable prompted form. We convert a large set of supervised datasets, each with multiple prompts with diverse wording. These prompted datasets allow for benchmarking the ability of a model to perform completely held-out tasks. We fine-tune a pretrained encoder-decoder model (Raffel et al., 2020; Lester et al., 2021) on this multitask mixture covering a wide variety of tasks. The model attains strong zero-shot performance on several standard datasets, often outperforming models up to 16x its size. Further, our approach attains strong performance on a subset of tasks from the BIG-bench benchmark, outperforming models up to 6x its size. All trained models are available at https://github.com/bigscience-workshop/t-zero and all prompts are available at https://github.com/bigscience-workshop/promptsource.
How Effective is Learning Style Material with E-Modules During the COVID-19 Pandemic?

The problem behind this research was the lack of learning resources for students in science subjects so that learning outcomes are less than optimal. This research was a research and development (research and development). The research trial subjects were fourth grade students. The data collection instrument used was in the form of an evaluation sheet (questionnaire). The data analysis used qualitative descriptive analysis and quantitative descriptive analysis. The results of the assessment of the e-module product were carried out based on six aspects, namely: content aspects of the field of study / with a percentage of achievement level of 86.00% with a predicate worthy; In terms of the learning design aspect, the percentage of achievement level is 87.60%, predicate worthy; In terms of the learning media aspect with the achievement level percentage of 89.00%, the predicate is feasible; in terms of the individual trial aspect, with the percentage level of achievement of 92.50% the predicate is very feasible; small group trial, with the percentage of achievement level of 94.50%, the predicate is very feasible, and in terms of the field trial aspect, the percentage of the achievement level is 95.50%, the predicate is very feasible. Thus the product in the form of e-module has a good level of validity and is suitable for use in the learning process on the subject force material of science subjects.