Reviewerly/exHarmony huggingface dataset:
CNN-Based Mask-Pose Fusion for Detecting Specific Persons on Heterogeneous Embedded Systems

In recent times, numerous convolutional neural network (CNN) based detection models have been proposed and have shown excellent performance. However, because these models are generally developed to detect objects in class units (e.g., person, car), additional training processes with numerous datasets are required to find a specific object. This paper proposes a model that accurately detects specific persons by using top clothing color information without any additional training processes. The proposed method combines CNN-based instance segmentation and pose estimation, utilizing all the advantages of each technique. To avoid redundant computations, these two schemes are implemented as a filtering-based sequential operation structure. As a result, the proposed method has a 92.57% of accuracy in detecting a specific person with only a slight processing speed decrease. Furthermore, in this paper, the proposed model is efficiently ported on the heterogeneous embedded platform (i.e., NVIDIA Jetson AGX Xavier) with a parallel processing technique to maximize the hardware utilization.
Deep Edge Computing for Videos

This paper provides a modular architecture with deep neural networks as a solution for realtime video analytics in an edge-computing environment.The modular architecture consists of two networks of Front-CNN (Convolutional Neural Network) and Back-CNN, where we adopt Shallow 3D CNN (S3D) as the Front-CNN and a pre-trained 2D CNN as the Back-CNN.The S3D (i.e., the Front CNN) is in charge of condensing a sequence of video frames into a feature map with three channels.That is, the S3D takes a set of sequential frames in the video shot as input and yields a learned 3 channel feature map (3CFM) as output.Since the 3CFM is compatible with the three-channel RGB color image format, we can use the output of the S3D (i.e., the 3CFM) as the input to a pre-trained 2D CNN of the Back-CNN for the transfer-learning.This serial connection of Front-CNN and Back-CNN architecture is end-to-end trainable to learn both spatial and temporal information of videos.Experimental results on the public datasets of UCF-Crime and UR-Fall Detection show that the proposed S3D-2DCNN model outperforms the existing methods and achieves state-of-the-art performance.Moreover, since our Front-CNN and Back-CNN modules have a shallow S3D and a light-weighted 2D CNN, respectively, it is suitable for real-time video recognition in edge-computing environments.We have implemented our CNN model on NVIDIA Jetson Nano Developer as an edge-computing device to show its real-time execution.
Geochemical Evaluation of Heavy Metals (Cd, Cr, Fe, and Mn) in Sediment of Shatt Al-Basrah, Iraq

In this study, the sediment of Shatt Al-Basrah canal, was evaluated to illustrate the distribution of 4 heavy metals Cd, Cr, Fe and Mn in sediments collected from 5 sites.The assessment of heavy metals was conducted using three indices; the geoaccumulation index (I-geo), the enrichment factor (E.F.) and Pollution Index (PI).According to I-geo, the sediments collected from all sampling locations were unpolluted by Cd, Cr and Fe, where their values are less than 0 (<0), except Mn ranged between 0.98 to 1.37, the Igeo values for Mn show that sediments of Shatt Al-Basrah are unpolluted to moderately polluted for all sampling locations.Based on The enrichment factor, the sediment of Shatt Al-Basrah canal are classified as followed; significant enrichment for Cd, moderate enrichment to significant enrichment for Cr and deficiency to minimal enrichment for Mn.PI, which is based on individual metal Concentrations, shows that all sampling sites have no pollution effect for Cd, Cr and Mn, except Fe, which cause Slightly pollution affect in all site.
Adoption and use of social media in academic libraries in South Africa

The purpose of this study was to investigate the extent of adoption and use of social media for client support by academic libraries in South Africa. A survey and content analysis were concurrently used to obtain relevant data to achieve the objectives of the study. The target population comprised seventy-eight library workers from twenty-six institutions, consisting of administrators of social media, librarians and IT technicians. A total of forty-eight respondents completed and returned the questionnaires, which were then analysed using descriptive statistics. Results revealed that a variety of social media platforms are available and used in most libraries in South Africa; the most common platforms are Facebook, Twitter, YouTube and blogs. The social media platforms are used to market library services, announce library news and improve service delivery. The information shared with the users relates to user information, library events and new services. Most libraries do not have policies and/or guidelines specific to the management of social media usage. Library workers associated many benefits with social media platforms, but diverse factors affect the adoption and use of social media in the libraries. The study makes several recommendations in relation to the adoption and use of social media in libraries and for further research in the subject domain.
Entanglement and U(D)-spin squeezing in symmetric multi-quDit systems and applications to quantum phase transitions in Lipkin–Meshkov–Glick D-level atom models

Abstract Collective spin operators for symmetric multi-quDit (namely identical D -level atom) systems generate a U( D ) symmetry. We explore generalizations to arbitrary D of SU(2)-spin coherent states and their adaptation to parity (multi-component Schrödinger cats), together with multi-mode extensions of NOON states. We write level, one- and two-quDit reduced density matrices of symmetric N -quDit states, expressed in the last two cases in terms of collective U( D )-spin operator expectation values. Then, we evaluate level and particle entanglement for symmetric multi-quDit states with linear and von Neumann entropies of the corresponding reduced density matrices. In particular, we analyze the numerical and variational ground state of Lipkin–Meshkov–Glick models of 3-level identical atoms. We also propose an extension of the concept of SU(2)-spin squeezing to SU( D ) and relate it to pairwise D -level atom entanglement. Squeezing parameters and entanglement entropies are good markers that characterize the different quantum phases, and their corresponding critical points, that take place in these interacting D -level atom models.
On the Impact of Spurious Correlation for Out-of-Distribution Detection

Modern neural networks can assign high confidence to inputs drawn from outside the training distribution, posing threats to models in real-world deployments. While much research attention has been placed on designing new out-of-distribution (OOD) detection methods, the precise definition of OOD is often left in vagueness and falls short of the desired notion of OOD in reality. In this paper, we present a new formalization and model the data shifts by taking into account both the invariant and environmental (spurious) features. Under such formalization, we systematically investigate how spurious correlation in the training set impacts OOD detection. Our results suggest that the detection performance is severely worsened when the correlation between spurious features and labels is increased in the training set. We further show insights on detection methods that are more effective in reducing the impact of spurious correlation, and provide theoretical analysis on why reliance on environmental features leads to high OOD detection error. Our work aims to facilitate better understanding of OOD samples and their formalization, as well as the exploration of methods that enhance OOD detection. Code is available at https://github.com/deeplearning-wisc/Spurious_OOD.
xGQA: Cross-Lingual Visual Question Answering

Recent advances in multimodal vision and language modeling have predominantly focused on the English language, mostly due to the lack of multilingual multimodal datasets to steer modeling efforts.In this work, we address this gap and provide xGQA, a new multilingual evaluation benchmark for the visual question answering task.We extend the established English GQA dataset (Hudson and Manning, 2019) to 7 typologically diverse languages, enabling us to detect and explore crucial challenges in cross-lingual visual question answering.We further propose new adapter-based approaches to adapt multimodal transformer-based models to become multilingual, and-vice versa-multilingual models to become multimodal.Our proposed methods outperform current state-of-the-art multilingual multimodal models (e.g., M 3 P) in zeroshot cross-lingual settings, but the accuracy remains low across the board; a performance drop of around 38 accuracy points in target languages showcases the difficulty of zero-shot cross-lingual transfer for this task.Our results suggest that simple cross-lingual transfer of multimodal models yields latent multilingual multimodal misalignment, calling for more sophisticated methods for vision and multilingual language modeling. 1
Identifying Morality Frames in Political Tweets using Relational Learning

Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions.The Moral Foundation Theory identifies five moral foundations, each associated with a positive and negative polarity.However, moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities.In this paper, we introduce morality frames, a representation framework for organizing moral attitudes directed at different entities, and come up with a novel and highquality annotated dataset of tweets written by US politicians.Then, we propose a relational learning model to predict moral attitudes towards entities and moral foundations jointly.We do qualitative and quantitative evaluations, showing that moral sentiment towards entities differs highly across political ideologies.
Spatial Interpolation of SPT with Artificial Neural Network

In large infrastructure projects, initial geotechnical investigation is conducted at large spacing (~ 100m to 250m), in which SPT is the common test performed while dynamic tests are limited in number.The preliminary planning and design of the buildings are performed based on this information.Hence, estimate of dynamic properties of soil (say, shear wave velocity) at building locations becomes necessary.This can be performed by estimation of SPT at building locations, by interpolation from borehole locations, and thereafter using correlation expressions for estimating shear wave velocity at building location.Interpolation of SPT has been handled earlier in literature with statistical and geospatial techniques.In this article, an artificial intelligence technique, namely, artificial neural network (ANN) is explored for addressing this problem.ANN allows multiple degrees of freedom to data and optimizes weights and biases of the network to yield the best possible estimates of the desired output, in this case, the SPT at intermediate locations.ANN is known to be robust in handling data with noise and thus would be suitable for this application.Five neighbouring points were found suitable for efficient and accurate spatial interpolation of SPT using ANN with two to three neurons in one hidden layer.The performance was very good (correlation higher than 0.9 and errors lower than 2) and better than the geo-statistical approaches reported in literature (correlation lower than 0.9 and errors higher than 6).Within the limits of the study, the number of degrees of freedom (varying from 9 to 37) of the ANN did not affect its generalization capability.
BiSECT: Learning to Split and Rephrase Sentences with Bitexts

An important task in NLP applications such as sentence simplification is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary.We introduce a novel dataset and a new model for this 'split and rephrase' task.Our BISECT training data consists of 1 million long English sentences paired with shorter, meaning-equivalent English sentences.We obtain these by extracting 1-2 sentence alignments in bilingual parallel corpora and then using machine translation to convert both sides of the corpus into the same language.BISECT contains higher quality training examples than previous Split and Rephrase corpora, with sentence splits that require more significant modifications.We categorize examples in our corpus, and use these categories in a novel model that allows us to target specific regions of the input sentence to be split and edited.Moreover, we show that models trained on BISECT can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations.
The Rise of Technocracy and the COVID-19 Pandemic in Taiwan: Courts, Human Rights, and the Protection of Vulnerable Populations

Abstract Driven by the need to address the immediate public health threats of the COVID-19 pandemic, this has seen a rise of the technocratic mode of governance around the world. A technocratic approach is evidence-based and relies upon the guidance of experts to respond to the public health crisis. The rise of technocracy reflects a utilitarian calculus that seeks to preserve the greater good. Taiwan’s pandemic response exemplifies the strengths and weaknesses of this type of governance. Based on an analysis of the relevant case law of the Taiwan Constitutional Court, legislation, and political developments this Article takes a legal-historical look and traces the current technocratic approach—defined for this Article as an experts-driven and procedural-driven process—which is a hallmark of Taiwan’s pandemic response. Examining Taiwan’s pandemic response through a human rights lens sheds light on a more complex relationship between the collective right to health and life, and the individual rights to health, work, privacy, and liberty during the pandemic.
Political Leaders’ Communication: A Twitter Sentiment Analysis during Covid-19 Pandemic

&lt;p class="Default"&gt;&lt;em&gt;&lt;span&gt;The pandemic made it critical for political leaders to intensify measures in fight against Covid-19 and one such measure was building trust among public through communication. With exponential growth in reach of social media, while state political leaders have progressively used internet for election campaigns, limited studies have explored as to how leaders use this medium to communicate during crisis, what kind of information do they share and what are common issues addressed. This paper, using qualitative research design, analyses Indian political leaders’ communication on Twitter. Sentiment Analysis was carried to identify and extract subjective information in leaders’ communication using 29 Indian political leaders, wherein 12,128 tweets were extracted. Subjectivity scores depicted more than half of leaders had shared fact-based information, and Polarity scores indicated that almost 90% of leaders shared positive or neutral information thus leading to an inference that leaders share more of facts based and positive or neutral information rather than statements in form of opinions.&lt;/span&gt;&lt;/em&gt;&lt;/p&gt;
Short quantum circuits in reinforcement learning policies for the vehicle routing problem

Quantum computing and machine learning have potential for symbiosis. However, in addition to the hardware limitations from current devices, there are still basic issues that must be addressed before quantum circuits can usefully incorporate with current machine learning tasks. We report a strategy for such an integration in the context of attention models used for reinforcement learning. Agents that implement attention mechanisms have successfully been applied to certain cases of combinatorial routing problems by first encoding nodes on a graph and then sequentially decoding nodes until a route is selected. We demonstrate that simple quantum circuits can be used in place of classical attention head layers while maintaining performance. Our method modifies attention mechanisms by replacing key and query vectors for every node with quantum states that are entangled before being measured. The resulting hybrid classical-quantum agent is tested in the context of vehicle routing problems where its performance is competitive with the original classical approach. We regard our model as a prototype that can be scaled up and as an avenue for further study on the role of quantum computing in reinforcement learning.
A Multicriteria Decision Making Taxonomy of IOT Security Challenging Factors

Internet of things (IoT) is leading a new digital age.IoT is regarded as the significant frontier that can improve almost all aspect of our lives.Currently, the IoT technology faces several challenges to academic researchers and industry practitioners, mainly that related with security of data.The objective of this study is to develop a prioritization-based taxonomy of the challenging factors that could hinders the security of IoT.By conducting the literature review and questionnaire survey studies 21 challenging factors were identified that are reported in existing literature and in real-world practices.Moreover, the identified challenging factors are mapped in the core domain of IoT (i.e.smart city, smart home, smart wearable's and smart health care); and apply the fuzzy-AHP approach to rank the identified challenging factors with respect to their criticality for security of IoT technology.The application of fuzzy-AHP is novel in this research area as it is successfully applied in other domains of information technology to address the multi-criterion decision making problems.This study is contributing by providing a prioritization-based taxonomy of the IoT security challenging factors that could help the practitioners and research community to revise and develop the new strategies for the secure IoT.
Phase diagram of quantum generalized Potts-Hopfield neural networks

Abstract We introduce and analyze an open quantum generalization of the q-state Potts-Hopfield neural network (NN), which is an associative memory model based on multi-level classical spins. The dynamics of this many-body system is formulated in terms of a Markovian master equation of Lindblad type, which allows to incorporate both probabilistic classical and coherent quantum processes on an equal footing. By employing a mean field description we investigate how classical fluctuations due to temperature and quantum fluctuations effectuated by coherent spin rotations affect the ability of the network to retrieve stored memory patterns. We construct the corresponding phase diagram, which in the low temperature regime displays pattern retrieval in analogy to the classical Potts-Hopfield NN. When increasing quantum fluctuations, however, a limit cycle phase emerges, which has no classical counterpart. This shows that quantum effects can qualitatively alter the structure of the stationary state manifold with respect to the classical model, and potentially allow one to encode and retrieve novel types of patterns.
CC-CERT: A Probabilistic Approach to Certify General Robustness of Neural Networks

In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks --- small modifications of the input that change the predictions. Besides rigorously studied $\ell_p$-bounded additive perturbations, semantic perturbations (e.g. rotation, translation) raise a serious concern on deploying ML systems in real-world. Therefore, it is important to provide provable guarantees for deep learning models against semantically meaningful input transformations. In this paper, we propose a new universal probabilistic certification approach based on Chernoff-Cramer bounds that can be used in general attack settings. We estimate the probability of a model to fail if the attack is sampled from a certain distribution. Our theoretical findings are supported by experimental results on different datasets.
Designing and Evaluating an Automatic Forensic Model for Fast Response of Cross-Border E-Commerce Security Incidents

The rapid development of cross-border e-commerce over the past decade has accelerated the integration of the global economy. At the same time, cross-border e-commerce has increased the prevalence of cybercrime, and the future success of e-commerce depends on enhanced online privacy and security. However, investigating security incidents is time- and cost-intensive as identifying telltale anomalies and the source of attacks requires the use of multiple forensic tools and technologies and security domain knowledge. Prompt responses to cyber-attacks are important to reduce damage and loss and to improve the security of cross-border e-commerce. This article proposes a digital forensic model for first incident responders to identify suspicious system behaviors. A prototype system is developed and evaluated by incident response handlers. The model and system are proven to help reduce time and effort in investigating cyberattacks. The proposed model is expected to enhance security incident handling efficiency for cross-border e-commerce.
Sentiment analysis of Twitter texts using Machine learning algorithms

Since the two last decades, social media networks have become a part of our daily life. Today, getting information from social media, tracking trends in social media, learning the feelings and emotions of people on social media is very essential. In this study, sentiment analysis was performed on Twitter text to learn about the subjective polarities of the writings. The polarities are positive, negative, and neutral. At the first stage of the sentiment analysis, a public data set has been obtained. Secondly, natural language processing techniques have been applied to make the data ready for machine learning training procedures. Lastly, sentiment analysis is performed by using three different machine learning algorithms. We reached 89% accuracy with Support Vector Machines, 88% accuracy with Random Forest, and 72% accuracy with Gaussian Naive Bayes classifier.
Interval-Valued Intuitionistic Fuzzy Decision-Making Method using Index Matrices and Application in Outsourcing

Selecting a suitable outsourcing service provider is a challenging problem that requires discussion among a group of experts.The problems of this type belongs to the area of multicriteria decision-making.Interval-valued intuitionistic fuzzy sets, which are an extension of intuitionistic fuzzy sets, are a capable tool in modeling uncertain problems.In this paper we will formulate an optimal interval-valued intuitionistic fuzzy multicriteria decision-making problem in outsourcing and propose a new approach for the selection of the most appropriate candidates; as well as a software program for its automated solution, based on our previous libraries.As an example of a case study, an application of the algorithm on real data from a refinery is demonstrated.