Reviewerly/exHarmony huggingface dataset:
Resource-Efficient High-Dimensional Entanglement Detection via Symmetric Projections

We introduce two families of criteria for detecting and quantifying the entanglement of a bipartite quantum state of arbitrary local dimension. The first is based on measurements in mutually unbiased bases and the second is based on equiangular measurements. Both criteria give a qualitative result in terms of the state's entanglement dimension and a quantitative result in terms of its fidelity with the maximally entangled state. The criteria are universally applicable since no assumptions on the state are required. Moreover, the experimenter can control the trade-off between resource-efficiency and noise-tolerance by selecting the number of measurements performed. For paradigmatic noise models, we show that only a small number of measurements are necessary to achieve nearly-optimal detection in any dimension. The number of global product projections scales only linearly in the local dimension, thus paving the way for detection and quantification of very high-dimensional entanglement.
Fractionally Delayed Bayesian Approximation Filtering under Non-Gaussian Noisy Environment

Gaussian filtering traditionally suffers from two major drawbacks: i) Gaussian approximation of the intrinsic non-Gaussian measurement noises and ii) ignoring delay in measurements. This paper designs an advanced Gaussian filtering algorithm for addressing the two drawbacks and improving the accuracy. The proposed method is abbreviated as GFMCFD, indicating Gaussian filtering under maximum correntropy (MC) criterion for fractionally delayed measurements. The MC criterion-based design enables the proposed GFMCFD to handle the non-Gaussian noises. Moreover, to deal with the delay, the proposed GFMCFD stochastically identifies the delay and uses the current measurement to estimate the desired state at a past instant, depending on the preidentified delay. Thereafter, it later updates the estimated state till the current time instant using state dynamics to perform real-time estimation. Interestingly, the proposed GFMCFD considers the delay as a fractional multiple of sampling interval. The improved accuracy of the proposed GFMCFD is validated for two nonlinear filtering problems.
Hybrid deep autoencoder network based adaptive cross guided bilateral filter for motion artifacts correction and denoising from MRI

Removal of motion artifacts from medical images is essential, because the respiration by patient causes degradation in artifact removal. Therefore, getting an MRI without any artifacts and noise is challenging. Previous techniques failed to preserve the image quality during denoising, thereby affecting the classification accuracy. To overcome such challenges, two modules are considered in this work: classification and denoising. The classification process is successfully performed by Hybrid Deep autoencoder-convolutional neural network (DAE-CNN). It classifies the original images into motion artifacts, available and normal images. Then, the available artifacts are removed using cross guided bilateral filter (CGBF). The optimal parameters required to improve the CGBF performance are selected using the hybrid optimization algorithm. The filter’s performance relies on the optimal spatial and range kernels selection. Finally, the classification and denoising-based results are evaluated using the BRATS 2015 dataset and achieved 95.84% accuracy and PSNR – 46.4, which is better than existing methods.
Elastic beam algorithm for generating circular cartograms

ABSTRACTABSTRACTThe circular cartogram, also known as the Dorling map, is a widely used tool for visualizing statistical data. It represents regions as circles with their areas in proportion to the statistical values and requires circle displacement to avoid overlap and maintain spatial relationships. In this paper, we propose a new approach for circular cartogram production that utilizes the elastic beam displacement algorithm in cartographic generalization. First, the initial circles are generated with their areas in proportion to the statistical values. Second, an elastic beam structure is built as a proximity graph based on the spatial relations between the circles. Third, the circles violating the quality requirements are considered to have a force on the nodes of a beam. Fourth, the elastic beam algorithm is applied to assign forces for each node to determine the new positions of the circles. Steps two through four are repeated until a circular cartogram that meets the defined quality requirements is obtained. The experiments indicate that the proposed approach can successfully generate circular cartograms without overlaps while maintaining topology and relative relationships with higher quality than existing approaches. Project website: https://github.com/TrentonWei/DorlingMapKEYWORDS: CartogramDorling mapdisplacementpopulation mappingenergy minimization AcknowledgmentsThe authors would like to thank Dr. Yuangang Liu, the editors and the anonymous reviewers for their helpful and constructive comments that greatly contributed to improve the manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and code availability statement and data depositionThe data and code that support the findings of this study are all openly available, website is: https://github.com/TrentonWei/DorlingMap.Additional informationFundingThis work was supported by the The National Natural Science Foundation of China [grant agreement No 41871378] and The Youth Innovation Promotion Association Foundation of Chinese Academy of Sciences [grant agreement No Y9C0060].
Data Stream Analytics

The human brain works in such a complex way that we have not yet managed to decipher its functional mysteries [...]
StatMix: Data Augmentation Method that Relies on Image Statistics in Federated Learning

Availability of large amount of annotated data is one of the pillars of deep learning success. Although numerous big datasets have been made available for research, this is often not the case in real life applications (e.g. companies are not able to share data due to GDPR or concerns related to intellectual property rights protection). Federated learning (FL) is a potential solution to this problem, as it enables training a global model on data scattered across multiple nodes, without sharing local data itself. However, even FL methods pose a threat to data privacy, if not handled properly. Therefore, we propose StatMix, an augmentation approach that uses image statistics, to improve results of FL scenario(s). StatMix is empirically tested on CIFAR-10 and CIFAR-100, using two neural network architectures. In all FL experiments, application of StatMix improves the average accuracy, compared to the baseline training (with no use of StatMix). Some improvement can also be observed in non-FL setups.
Select, Extend, and Generate: Generative Knowledge Selection for Open-Domain Dialogue Response Generation

Incorporating external commonsense knowledge can enhance machines' cognition and facilitate informative dialogues. However, current commonsense knowledge-grounded dialogue generation works can only select knowledge from a finite set of candidates retrieved by information retrieval (IR) tools. This paradigm suffers from: 1) The knowledge candidate space is limited because IR tools can only retrieve existing knowledge from the given knowledge base, and the model can only use the retrieved knowledge; 2) The knowledge selection procedure lacks enough interpretability to explain the selected result. Moreover, with the increasing popularity of pre-trained language models (PLMs), many knowledge selection methods of non-PLM models have become incapable because of the input/structure restrictions of PLMs. To this end, we propose a simple but elegant SEG-CKRG, and introduce a novel PLM-friendly Generative Knowledge Selection (GenSel) to select knowledge via a generative procedure. Besides selecting the knowledge facts from the retrieved candidate set, GenSel can also generate newly extended knowledge. GenSel also improves interpretability because the output of the knowledge selection is a natural language text. Finally, SEG-CKRG uses GPT-2 as the backbone language model. Extensive experiments and analyses on a Chinese dataset have verified the superior performance of SEG-CKRG.
Making Robotic Swarms Trustful: A Blockchain-Based Perspective

Abstract Lately, the importance of swarm robotics has been recognized in a wide range of areas, including logistics, surveillance, disaster management, agriculture, and other industrial applications. The swarm intelligence introduced by the existing paradigm of artificial intelligence and machine learning often ignores the aspect of providing security and reliability guarantees. Consider a futuristic scenario wherein self-driving cars will transport people, self-driving trucks will carry cargo between warehouses, and a combination of legged robots/drones will ship cargo from warehouses to doorsteps. In the case of such a heterogeneous swarm of robots, it is crucial to ensure a trustful and reliable operating platform for smooth coordination, collaborative decision-making via appropriate consensus, and seamless information sharing while ensuring data security. In this direction, blockchain has been proven to be an effective technology that maintains the transactions (records) in a trustful manner after being validated through consensus. This guarantees accountability, transparency, and trust concerning the storage, safeguarding, and sharing of information among the parties. In this paper, we provide a walkthrough demonstrating the feasibility of using blockchain technology to make the robotic swarm trustful systems in their adoption to critical applications at large-scale. We highlight the pros and cons of the use of cloud vis-a-vis blockchain in swarm robotics. Finally, we present various future research opportunities pertaining to the adoption of blockchain technology in swarm robotics applications.
Hidden Backdoor Attack Against Deep Learning-Based Wireless Signal Modulation Classifiers

Recently, DL has been exploited in wireless communications such as modulation classification. However, due to the openness of wireless channel and unexplainability of DL, it is also vulnerable to adversarial attacks. In this correspondence, we investigate a so called hidden backdoor attack to modulation classification, where the adversary puts elaborately designed poisoned samples on the basis of IQ sequences into training dataset. These poisoned samples are hidden because it could not be found by traditional classification methods. And poisoned samples are same to samples with triggers which are patched samples in feature space. We show that the hidden backdoor attack can reduce the accuracy of modulation classification significantly with patched samples. At last, we propose activation cluster to detect abnormal samples in training dataset.
Automatic Essay Scoring Using Data Augmentation in Bahasa Indonesia

Essay is one of the assessments to find out the abilities of students in depth. UKARA is an automatic essay scoring development that combines NLP and machine learning. This study uses the datasets provided for the UKARA challenge which consists of 2 types, datasets A and B. The dataset provided is still small for the model creation process so that it is one of the causes of the resulting model is not optimal. This research focuses on the process of adding or augmenting data using EDA (Easy Data Augmentation Techniques). There are four methods applied, namely Synonym Replacement (SR), Random Insertion (RI), Random Swab (RS), and Random Deletion (RD). The data is used for model creation by using the BiLSTM method. Performa model evaluated using confusion matrix with nilai accyouracy, precision, recall dan f-measure.The results showed that the dataset A without augmentation using k-fold cross validation produced the highest accuracy value with a value of 85.07%. While the results in data B show EDA insert with k-fold cross validation of 72.78%.
Enhancing 360 Video Streaming through Salient Content in Head-Mounted Displays

Predicting where users will look inside head-mounted displays (HMDs) and fetching only the relevant content is an effective approach for streaming bulky 360 videos over bandwidth-constrained networks. Despite previous efforts, anticipating users’ fast and sudden head movements is still difficult because there is a lack of clear understanding of the unique visual attention in 360 videos that dictates the users’ head movement in HMDs. This in turn reduces the effectiveness of streaming systems and degrades the users’ Quality of Experience. To address this issue, we propose to extract salient cues unique in the 360 video content to capture the attentive behavior of HMD users. Empowered by the newly discovered saliency features, we devise a head-movement prediction algorithm to accurately predict users’ head orientations in the near future. A 360 video streaming framework that takes full advantage of the head movement predictor is proposed to enhance the quality of delivered 360 videos. Practical trace-driven results show that the proposed saliency-based 360 video streaming system reduces the stall duration by 65% and the stall count by 46%, while saving 31% more bandwidth than state-of-the-art approaches.
Exploration of Sindhi Corpus Through Statistical Analysis on the Basis of Reality

Objectives:The Sindhi language is given more importance in Sindh's educational institutions than other regional languages, and the majority of the population uses it in today's mobile programs, letters, text messages and other text conversations.Research is needed to analyze the Sindhi corpus, as communication over computer systems and mobile phones is growing significantly.This research study focuses on the Sindhi alphabet and performs different tasks on the corpus.Methods: Data collection was conducted from available resources, and a corpus was created in Sindhi and English.Twenty patterns of letters are used, three dot alignments are used in the letters, and six symbols are used for making letters.After the collection, data was explored and analyzed with different tasks.Findings: The corpus of Sindhi text is being built due to its importance for language, linguistics and other developments in NLP.This research study focuses on statically analyzing the Sindhi-English corpus through reality basis, finding that there are two small words ‫۽(‬ and ‫)۾‬ and three biggest words ‫اﻧﮕﻠﯾﻧڊ(‬ ‫ﭘﺎڪﺳﺗﺎن,‬ and ‫.)ﮘﺎﻟﮭﯾون‬The letter ‫'آ'‬ is used as a single letter in Sindhi alphabets, with the minimum frequently occurring letter being consonant and the maximum frequently being ‫ئ.‬ vowel.Novelty: Text analysis is an important area in data mining and in other research, and this research study focuses on statically analyzing the Sindhi-English corpus through statically on reality basis.The author explores orthography and Sindhi composition of copra, and recommends that the Romanized languages data be used in Sindhi as well.Preprocessing is not easy due to lack of resources, and the character conversion model has generated two languages.
CP-ABE Optimization via the Flexible Integration of Access Policies Containing Multiple Shared Subpolicies

Ciphertext-policy attribute-based encryption (CP-ABE) is a cryptographic scheme that is suitable for cloud storage and can realize secure data sharing. However, in most existing CP-ABE schemes, the encryption computational overhead is proportional to the number of attributes in the access policy. Therefore, reducing the computational overhead of the scheme’s encryption is an important consideration to improve its efficiency. Furthermore, in the scenario where the access control policies corresponding to different data shared by the same data owner have multiple shared subpolicies, there is still further optimizing space for encryption. This study proposes an optimization approach for CP-ABE via the flexible integration of access policies with multiple shared subpolicies. Regarding the root nodes of policies being both the same and different, we provide optimal policy integration methods to reduce the overall encryption computation cost of the data associated with these access policies. Under the premise of preserving the original security of the CP-ABE scheme, this scheme avoids repeated calculation related to the shared subpolicies during encryption, thus improving the scheme’s efficiency. Finally, the correctness and feasibility of the scheme are examined and verified by theoretical analysis and experiments.
IMRPOVING STUDENTS’ MOTIVATION IN LEARNING ENGLISH USING DIGITAL PLATFORM : A LESSON STUDY IN SMK MUHAMMADIYAH BUMIAYU

The success of learning activity is influenced by students’ motivation, teacher should be creative in order to enhance students’ enthusiasm, this may be accomplished by using digital platform such as google classroom, padlet, mentimeter, quizizz and classtools.net. Lesson study can be used to determine whether or not the usage of the digital platform is successful, that is implemented in a collaborative and sustainable way. This lesson study is implemented using a Project-based Learning (PjBL), an inquiry approach and students worksheet. This study used a qualitative approach with the descriptive analysis method. There are three stages of lesson study activities; plan, do, and see. The subject of the study was students of XI AKL SMK Muhammadiyah Bumiayu in academic year 2021/2022. The implementation of digital platform in teaching and learning activity was thoroughly well-conducted. through digital platform the students’ motivation is increasing, This can be seen from student enthusiasm, students’ collaboration, student involvement, and student interest in learning new applications.
On the Lift, Related Privacy Measures, and Applications to Privacy–Utility Trade-Offs

This paper investigates lift, the likelihood ratio between the posterior and prior belief about sensitive features in a dataset. Maximum and minimum lifts over sensitive features quantify the adversary's knowledge gain and should be bounded to protect privacy. We demonstrate that max- and min-lifts have a distinct range of values and probability of appearance in the dataset, referred to as lift asymmetry. We propose asymmetric local information privacy (ALIP) as a compatible privacy notion with lift asymmetry, where different bounds can be applied to min- and max-lifts. We use ALIP in the watchdog and optimal random response (ORR) mechanisms, the main methods to achieve lift-based privacy. It is shown that ALIP enhances utility in these methods compared to existing local information privacy, which ensures the same (symmetric) bounds on both max- and min-lifts. We propose subset merging for the watchdog mechanism to improve data utility and subset random response for the ORR to reduce complexity. We then investigate the related lift-based measures, including ℓ1-norm, χ2-privacy criterion, and α-lift. We reveal that they can only restrict max-lift, resulting in significant min-lift leakage. To overcome this problem, we propose corresponding lift-inverse measures to restrict the min-lift. We apply these lift-based and lift-inverse measures in the watchdog mechanism. We show that they can be considered as relaxations of ALIP, where a higher utility can be achieved by bounding only average max- and min-lifts.
Basic direction and realization path of PE teaching innovation in PSS based on deep learning model

At present, the traditional model of PE in PSS (PSS)has seriously affected the quality of PE teaching in PSS and the perception of PE among primary and secondary school students. Because of the urgent need for innovation in PE in PSS, this study proposes the LSTM model to achieve an accurate prediction of the innovation direction of PE in PSS. Based on the LSTM model, the user behavior is classified by extracting the important features of the innovation direction. Expression to achieve accurate prediction of the future development direction of PE. Using the data confusion matrix to estimate the prediction accuracy of the LSTM model, the four evaluation indicators of Accuracy, Precision, F1, and AUC are 0.0532~0.2323 higher than the baseline model. The prediction results of PE teaching innovation in PSS from three aspects of teaching thought, teaching content, teaching objectives and essence are output, which has obvious guiding significance for the overall optimization of PE classrooms in PSS. This result shows that the LSTM prediction model has important practical value.
Voice assistants in private households: a conceptual framework for future research in an interdisciplinary field

Abstract The present study identifies, organizes, and structures the available scientific knowledge on the recent use and the prospects of Voice Assistants (VA) in private households. The systematic review of the 207 articles from the Computer, Social, and Business and Management research domains combines bibliometric with qualitative content analysis. The study contributes to earlier research by consolidating the as yet dispersed insights from scholarly research, and by conceptualizing linkages between research domains around common themes. We find that, despite advances in the technological development of VA, research largely lacks cross-fertilization between findings from the Social and Business and Management Sciences. This is needed for developing and monetizing meaningful VA use cases and solutions that match the needs of private households. Few articles show that future research is well-advised to make interdisciplinary efforts to create a common understanding from complementary findings—e.g., what necessary social, legal, functional, and technological extensions could integrate social, behavioral, and business aspects with technological development. We identify future VA-based business opportunities and propose integrated future research avenues for aligning the different disciplines’ scholarly efforts.
Automated coplanarity inspection of BGA solder balls by structured light

The coplanarity of BGA (Ball Grid Array) solder balls is critical for reliable connectivity in semi-conductor units. However, existing ball height inspection techniques require high-cost equipment and skilled operators, which are not feasible for using in real-time manufacturing process. In this paper, a fast measurement method of coplanarity inspection of solder balls is developed based on structured light technique. The hardware of the system includes a camera and a projector. The digital-light-processing (DLP) projector emits programmed image patterns and the digital complementary-metal-oxide-semiconductor (CMOS) camera captures distorted image patterns on solder balls. The image processing software is developed to compare the original and distorted image patterns to reconstruct the three-dimensional (3D) solder balls. The method has been calibrated extensively and exhibits accuracy within 5 μm mean squared error compared to ground truth values determined by X-ray computed tomography. The proposed method achieves reliable, in-line ball height measurement and could be potentially used for real-time in-line coplanarity inspection of BGA chip during manufacturing.