Reviewerly/exHarmony huggingface dataset:
Experimentally optimizing QKD rates via nonlocal dispersion compensation

Abstract Quantum key distribution (QKD) enables unconditionally secure communication guaranteed by the laws of physics. The last decades have seen tremendous efforts in making this technology feasible under real-life conditions, with implementations bridging ever longer distances and creating ever higher secure key rates. Readily deployed glass fiber connections are a natural choice for distributing the single photons necessary for QKD both in intra- and intercity links. Any fiber-based implementation however experiences chromatic dispersion which deteriorates temporal detection precision. This ultimately limits maximum distance and achievable key rate of such QKD systems. In this work, we address this limitation to both maximum distance and key rate and present an effective and easy-to-implement method to overcome chromatic dispersion effects. By exploiting entangled photons’ frequency correlations, we make use of nonlocal dispersion compensation to improve the photons’ temporal correlations. Our experiment is the first implementation utilizing the inherently quantum-mechanical effect of nonlocal dispersion compensation for QKD in this way. We experimentally show an increase in key rate from 6.1 to 228.3 bits/s over 6.46 km of telecom fiber. Our approach is extendable to arbitrary fiber lengths and dispersion values, resulting in substantially increased key rates and even enabling QKD in the first place where strong dispersion would otherwise frustrate key extraction at all.
Vehicle Detection and Type Classification Based on CNN-SVM

In this paper, we propose vehicle detection and classification in a real road environment using a modified and improved AlexNet. Among the various challenges faced, the problem of poor robustness in extracting vehicle candidate regions through a single feature is solved using the YOLO deep learning series algorithm used to propose potential regions and to further improve the speed of detection. For this, the lightweight network Yolov2-tiny is chosen as the location network. In the training process, anchor box clustering is performed based on the ground truth of the training set, which improves its performance on the specific dataset. The low classification accuracy problem after template-based feature extraction is solved using the optimal feature description extracted through convolution neural network learning. Moreover, based on AlexNet, through adjusting parameters, an improved algorithm was proposed whose model size is smaller and classification speed is faster than the original AlexNet. Spatial Pyramid Pooling (SPP) is added to the vehicle classification network which solves the problem of low accuracy due to image distortion caused by image resizing. By combining CNN with SVM and normalizing features in SVM, the generalization ability of the model was improved. Experiments show that our method has a better performance in vehicle detection and type classification.
Stance detection using improved whale optimization algorithm

Abstract News is a medium that notifies people about the events that had happened worldwide. The menace of fake news on online platforms is on the rise which may lead to unwanted events. The majority of fake news is spread through social media platforms, since these platforms have a great reach. To identify the credibility of the news, various spam detection methods are generally used. In this work, a new stance detection method has been proposed for identifying the stance of fake news. The proposed stance detection method is based on the capabilities of an improved whale optimization algorithm and a multilayer perceptron. In the proposed model, weights and biases of the multilayer perceptron are updated using an improved whale optimization algorithm. The efficacy of the proposed optimized neural network has been tested on five benchmark stance detection datasets. The proposed model shows better results over all the considered datasets. The proposed approach has theoretical implications for further studies to examine the textual data. Besides, the proposed method also has practical implications for developing systems that can result conclusive reviews on any social problems.
Adaptation in Information Search and Decision-Making under Time Constraints

Prior work in IR has found that searchers under time constraints may adapt their search processes and perceive their task or their performance differently. In many of these prior studies, the task descriptions implicitly or explicitly conveyed an expectation of the amount of information needed to satisfy the task requirements in terms of number of pages (e.g., find N webpages on topic X) or the time to spend on the task (e.g., search until time is up) rather than allowing the participant to determine how much information was needed. In this lab-based study, we investigated the effects of time constraints on information search and decision-making. Participants completed a series of decision-making tasks with half of the participants receiving a 5-minute time constraint and half given no time guidance. They were asked to make good, specific recommendations for a friend, and they had considerable latitude in deciding how much information they needed. Results showed that participants in the time constraint condition made their decisions faster but there were few significant differences in measures of search behaviors between the time constraint conditions (RQ1). Qualitative analysis indicated that participants adapted their decision task by varying their recommendations in their specificity, justification strength, and contents in both time conditions (RQ2). Finally, we found evidence that the impact of the time constraint on time- and task-related perceptions was moderated by the extent to which participants adapted their decision task (RQ3).
Combining CNN and LSTM for activity of daily living recognition with a 3D matrix skeleton representation

Abstract In socially assistive robotics, human activity recognition plays a central role when the adaptation of the robot behavior to the human one is required. In this paper, we present an activity recognition approach for activities of daily living based on deep learning and skeleton data. In the literature, ad hoc features extraction/selection algorithms with supervised classification methods have been deployed, reaching an excellent classification performance. Here, we propose a deep learning approach, combining CNN and LSTM, that exploits both the learning of spatial dependencies correlating the limbs in a skeleton 3D grid representation and the learning of temporal dependencies from instances with a periodic pattern that works on raw data and so without requiring an explicit feature extraction process. These models are proposed for real-time activity recognition, and they are tested on the CAD-60 dataset. Results show that the proposed model behaves better than an LSTM model thanks to the automatic features extraction of the limbs’ correlation. “New Person” results show that the CNN-LSTM model achieves $$95.4\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>95.4</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math> of precision and $$94.4\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>94.4</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math> of recall, while the “Have Seen” results are $$96.1\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>96.1</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math> of precision and $$94.7\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>94.7</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math> of recall.
AgingMapGAN (AMGAN): High-Resolution Controllable Face Aging with Spatially-Aware Conditional GANs

Existing approaches and datasets for face aging produce results skewed towards the mean, with individual variations and expression wrinkles often invisible or overlooked in favor of global patterns such as the fattening of the face. Moreover, they offer little to no control over the way the faces are aged and can difficultly be scaled to large images, thus preventing their usage in many real-world applications. To address these limitations, we present an approach to change the appearance of a high-resolution image using ethnicity-specific aging information and weak spatial supervision to guide the aging process. We demonstrate the advantage of our proposed method in terms of quality, control, and how it can be used on high-definition images while limiting the computational overhead.
Surface Defect Detection of Electric Power Equipment in Substation Based on Improved YOLOV4 Algorithm

The failure of electric power equipment in substation may lead to a large-scale uncontrollable power outage, which will cause immeasurable loss to the national economy and industrial production. The target detection method based on deep learning can effectively obtain the surface defect such as crack, rust, and oil leakage of electric power equipment, thereby improving the quality of unattended operation and maintenance. However, this method has difficulty in positioning especially when the image acquisition equipment has traveling error. In this paper, the YOLO-V4 backbone network is improved to solve the positioning difficulty in electric power equipment target detection, and the focal loss function is induced to promote the low detection accuracy due to imbalance between the positive and negative sample. Finally, the improved YOLO-V4 algorithm for surface defect detection of electric power equipment is implemented.
Semantic micro-contributions with decentralized nanopublication services

While the publication of Linked Data has become increasingly common, the process tends to be a relatively complicated and heavy-weight one. Linked Data is typically published by centralized entities in the form of larger dataset releases, which has the downside that there is a central bottleneck in the form of the organization or individual responsible for the releases. Moreover, certain kinds of data entries, in particular those with subjective or original content, currently do not fit into any existing dataset and are therefore more difficult to publish. To address these problems, we present here an approach to use nanopublications and a decentralized network of services to allow users to directly publish small Linked Data statements through a simple and user-friendly interface, called Nanobench, powered by semantic templates that are themselves published as nanopublications. The published nanopublications are cryptographically verifiable and can be queried through a redundant and decentralized network of services, based on the grlc API generator and a new quad extension of Triple Pattern Fragments. We show here that these two kinds of services are complementary and together allow us to query nanopublications in a reliable and efficient manner. We also show that Nanobench makes it indeed very easy for users to publish Linked Data statements, even for those who have no prior experience in Linked Data publishing.
Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data

All groups of people felt the impact of the COVID-19 pandemic. This situation triggers anxiety, which is bad for everyone. The government's role is very influential in solving these problems with its work program. It also has many pros and cons that cause public anxiety. For that, it is necessary to detect anxiety to improve government programs that can increase public expectations. This study applies machine learning to detecting anxiety based on social media comments regarding government programs to deal with this pandemic. This concept will adopt a sentiment analysis in detecting anxiety based on positive and negative comments from netizens. The machine learning methods implemented include K-NN, Bernoulli, Decision Tree Classifier, Support Vector Classifier, Random Forest, and XG-boost. The data sample used is the result of crawling YouTube comments. The data used amounted to 4862 comments consisting of negative and positive data with 3211 and 1651. Negative data identify anxiety, while positive data identifies hope (not anxious). Machine learning is processed based on feature extraction of count-vectorization and TF-IDF. The results showed that the sentiment data amounted to 3889 and 973 in testing, and training with the greatest accuracy was the random forest with feature extraction of vectorization count and TF-IDF of 84.99% and 82.63%, respectively. The best precision test is K-NN, while the best recall is XG-Boost. Thus, Random Forest is the best accurate to detect someone's anxiety based-on data from social media.
Machine learning methods for toxic comment classification: a systematic review

Abstract Nowadays users leave numerous comments on different social networks, news portals, and forums. Some of the comments are toxic or abusive. Due to numbers of comments, it is unfeasible to manually moderate them, so most of the systems use some kind of automatic discovery of toxicity using machine learning models. In this work, we performed a systematic review of the state-of-the-art in toxic comment classification using machine learning methods. We extracted data from 31 selected primary relevant studies. First, we have investigated when and where the papers were published and their maturity level. In our analysis of every primary study we investigated: data set used, evaluation metric, used machine learning methods, classes of toxicity, and comment language. We finish our work with comprehensive list of gaps in current research and suggestions for future research themes related to online toxic comment classification problem.
Transitions in entanglement complexity in random quantum circuits by measurements

Random Clifford circuits doped with non Clifford gates exhibit transitions to universal entanglement spectrum statistics[1] and quantum chaotic behavior. In [2] we proved that the injection of $O(n)$ non Clifford gates into a $n$-qubit Clifford circuit drives the transition towards the universal value of the purity fluctuations. In this paper, we show that doping a Clifford circuit with $O(n)$ single qubit non Clifford measurements is both necessary and sufficient to drive the transition to universal fluctuations of the purity.
Gradient local auto-correlation features for depth human action recognition

Abstract Human action classification is a dynamic research topic in computer vision and has applications in video surveillance, human–computer interaction, and sign-language recognition. This paper aims to present an approach for the categorization of depth video oriented human action. In the approach, the enhanced motion and static history images are computed and a set of 2D auto-correlation gradient feature vectors is obtained from them to describe an action. Kernel-based Extreme Learning Machine is used with the extracted features to distinguish the diverse action types promisingly. The proposed approach is thoroughly assessed for the action datasets namely MSRAction3D, DHA, and UTD-MHAD. The approach achieves an accuracy of 97.44% for MSRAction3D, 99.13% for DHA, and 88.37% for UTD-MHAD. The experimental results and analysis demonstrate that the classification performance of the proposed method is considerable and surpasses the state-of-the-art human action classification methods. Besides, from the complexity analysis of the approach, it is turn out that our method is consistent for the real-time operation with low computational complexity.
DEJIT: A Differential Evolution Algorithm for Effort-Aware Just-in-Time Software Defect Prediction

Software defect prediction is an effective approach to save testing resources and improve software quality, which is widely studied in the field of software engineering. The effort-aware just-in-time software defect prediction (JIT-SDP) aims to identify defective software changes in limited software testing resources. Although many methods have been proposed to solve the JIT-SDP, the effort-aware prediction performance of the existing models still needs to be further improved. To this end, we propose a differential evolution (DE) based supervised method DEJIT to build JIT-SDP models. Specifically, first we propose a metric called density-percentile-average (DPA), which is used as optimization objective on the training set. Then, we use logistic regression (LR) to build a prediction model. To make the LR obtain the maximum DPA on the training set, we use the DE algorithm to determine the coefficients of the LR. The experiment uses defect data sets from six open source projects. We compare the proposed method with state-of-the-art four supervised models and four unsupervised models in cross-validation, cross-project-validation and timewise-cross-validation scenarios. The empirical results demonstrate that the DEJIT method can significantly improve the effort-aware prediction performance in the three evaluation scenarios. Therefore, the DEJIT method is promising for the effort-aware JIT-SDP.
Blockchain-based Land Certificate Management in Indonesia

Indonesia is a densely populated country with a population of 260 million, making Indonesia the fourth most populous country in the world. The continued rapid development of the economy in Indonesia has made land an asset that provides benefits. In Indonesia, land ownership that has the highest legal power is proven by a Certificate of Ownership or in indonesian is called as Sertifikat Hak Milik (SHM). Meanwhile, the land titling process is often complex and lengthy, and because of the processing carried out by government agencies in handling a variety of different documents, some structured people commit fraud. The impact is that the model currently used does not have good governance. This research offers a Blockchain-based solution in providing data harmony and openness, lightening in data access, permanent records management, and most importantly it is a solution that is cheaper and faster. This research offers a step-by-step model of Blockchain selection beginning from the public Blockchain ledger which will continuously incorporate two degrees of Hybrid Blockchain. The smart contract design of the Public Blockchain is provided in detail as well as the use of Ethereum in implementing its prototype system. In the experimental test using the local Ethereum test network directly to show the effectiveness of the system. The results of the analysis show that the model offered can reduce the costs required for processing information, the number of trips required, and result in easy access to important information. With the implementation of Blockchain, efforts to digitize land rights in Indonesia can increase.
Understanding the Impact that Response Failure has on How Users Perceive Anthropomorphic Conversational Service Agents: Insights from an Online Experiment

Conversational agents (CAs) have attracted the interest from organizations due to their potential to provide automated services and the feeling of humanlike interaction.Emerging studies on CAs have found that humanness has a positive impact on customer perception and explored approaches for their anthropomorphic design, which comprises both their appearance and behavior.While these studies provide valuable knowledge on how to design humanlike CAs, we still do not sufficiently understand this technology's limited conversational capabilities and their potentially detrimental impact on user perception.These limitations often lead to frustrated users and discontinued CAs in practice.We address this gap by investigating the impact of response failure, which we understand a CA's inability to provide a meaningful reply, in a service context.To do so, we draw on the computers are social actors paradigm and the theory of the uncanny valley.Via an experiment with 169 participants, we found that 1) response failure harmed the extent to which people perceived CAs as human and increased their feelings of uncanniness, 2) humanness (uncanniness) positively (negatively) influenced familiarity and service satisfaction, and 3) the response failure had a significant negative impact on user perception yet did not lead to a sharp drop as the uncanny valley theory posits.Thus, our study contributes to better explaining the impact that text-based CAs' failure to respond has on customer perception and satisfaction in a service context in relation to the agents' design.
Object Priors for Classifying and Localizing Unseen Actions

Abstract This work strives for the classification and localization of human actions in videos, without the need for any labeled video training examples. Where existing work relies on transferring global attribute or object information from seen to unseen action videos, we seek to classify and spatio-temporally localize unseen actions in videos from image-based object information only. We propose three spatial object priors, which encode local person and object detectors along with their spatial relations. On top we introduce three semantic object priors, which extend semantic matching through word embeddings with three simple functions that tackle semantic ambiguity, object discrimination, and object naming. A video embedding combines the spatial and semantic object priors. It enables us to introduce a new video retrieval task that retrieves action tubes in video collections based on user-specified objects, spatial relations, and object size. Experimental evaluation on five action datasets shows the importance of spatial and semantic object priors for unseen actions. We find that persons and objects have preferred spatial relations that benefit unseen action localization, while using multiple languages and simple object filtering directly improves semantic matching, leading to state-of-the-art results for both unseen action classification and localization.
The Machine as an Extension of the Body: When Identity, Immersion and Interactive Design Serve as Both Resource and Limitation for the Disabled

This research explores how the technological affordances of emerging social virtual environments and VR platforms where individuals from an online disability community are represented in avatar form, correspond to these users’ development of embodied identity, ability, and access to work and social communities. The visual attributes of these avatars, which can realistically reflect the user’s physical self or divert from human form entirely, raise interesting questions regarding the role identity plays in the workplace, be it gender, race, age, weight, or visible disability. Additionally, the technology itself becomes fundamental to identity as the increasing use of artificial intelligence (AI), motion capture, and speech-to-text/text-to-speech technologies create digital capabilities that become part of an individual’s identity. This raises further questions about how virtual world technologies can both increase and potentially create barriers to accessibility for individuals who find freedom in their technologically embodied surrogates.
Zero-Shot Cross-lingual Semantic Parsing

Recent work in cross-lingual semantic parsing has successfully applied machine translation to localize parsers to new languages.However, these advances assume access to highquality machine translation systems and word alignment tools.We remove these assumptions and study cross-lingual semantic parsing as a zero-shot problem, without parallel data (i.e., utterance-logical form pairs) for new languages.We propose a multi-task encoderdecoder model to transfer parsing knowledge to additional languages using only Englishlogical form paired data and in-domain natural language corpora in each new language.Our model encourages language-agnostic encodings by jointly optimizing for logical-form generation with auxiliary objectives designed for cross-lingual latent representation alignment.Our parser performs significantly above translation-based baselines and, in some cases, competes with the supervised upper-bound. 1