Reviewerly/exHarmony huggingface dataset:
Machine Learning-Based Emotional Recognition in Surveillance Video Images in the Context of Smart City Safety

The effective extraction of deep information from surveillance video lays the basis for smart city safety. However, the surveillance video images contain complex targets, whose expression changes are difficult to capture. The traditional face expression recognition methods or sentiment analysis algorithms have a poor application effect. Based on machine learning (ML), this paper explores the emotional recognition in surveillance video images in the context of smart city safety. Firstly, the potential textures of surveillance video images were extracted under multi-order double cross (MODC) mode, and the optical flow features of facial expressions were detected in these images. Next, a facial expression recognition model was constructed based on the DeepID convolutional neural network (CNN), and an emotional semantic space was established for the face images in surveillance video. The proposed method was proved effective through experiments. The research results provide a reference for emotional recognition in images of other fields.
Reconstruction of Total Solar Irradiance by Deep Learning

The Earth's primary source of energy is the radiant energy generated by the Sun, which is referred to as solar irradiance, or total solar irradiance (TSI) when all of the radiation is measured. A minor change in the solar irradiance can have a significant impact on the Earth's climate and atmosphere. As a result, studying and measuring solar irradiance is crucial in understanding climate changes and solar variability. Several methods have been developed to reconstruct total solar irradiance for long and short periods of time; however, they are physics-based and rely on the availability of data, which does not go beyond 9,000 years. In this paper we propose a new method, called TSInet, to reconstruct total solar irradiance by deep learning for short and long periods of time that span beyond the physical models' data availability. On the data that are available, our method agrees well with the state-of-the-art physics-based reconstruction models. To our knowledge, this is the first time that deep learning has been used to reconstruct total solar irradiance for more than 9,000 years.
Qualitative analysis of attitudes to distance learning in the context of the COVID-19 pandemic

Topicality of the study of the attitude to the distance learning format is due to the need to study the impact of such interaction in the “pedagogue-studentˮ system on the quality, motivation and overall satisfaction of students with the learning process. The study involved 120 people from 6 six higher education institutions in Moscow. The use of content analysis allowed us to identify 8 significant contexts (areas) around which the statements of the study participants are grouped: “Roadˮ, “Teachersˮ, “Returnˮ, “Distance learningˮ, “Full-time, full-time attendingˮ, “Training, training formatˮ, “Provided, changedˮ, “Qualityˮ. The results obtained indicate that the main disadvantages of the distance learning format, according to the respondents, are the lack of live communication with teachers, a decrease in the level of motivation and self-organisation, which ultimately has a negative impact on the psychoemotional state of students and the quality of material assimilation. At the same time, among the obvious advantages, there is a reduction in transport and time costs for the road to the place of study, as well as the opportunity to study the material at a convenient time. The results obtained can be used in the development of distance learning programmes for students of higher educational institutions of the Russian Federation.
Talking Face Generation via Learning Semantic and Temporal Synchronous Landmarks

Given a speech clip and facial image, the goal of talking face generation is to synthesize a talking face video with accurate mouth synchronization and natural face motion. Recent progress has proven the effectiveness of the landmarks as the intermediate information during talking face generation. However, the large gap between audio and visual modalities makes the prediction of landmarks challenging and limits generation ability. This paper proposes a semantic and temporal synchronous landmark learning method for talking face generation. First, we propose to introduce a word detector to enforce richer semantic information. Then, we propose to preserve the temporal synchronization and consistency between landmarks and audio via the proposed temporal residual loss. Lastly, we employ a U-Net generation network with adaptive reconstruction loss to generate facial images for the predicted landmarks. Experimental results on two benchmark datasets LRW and GRID demonstrate the effectiveness of our model compared to the state-of-the-art methods of talking face generation.
LISA: a lean information service architecture for SLA management in multi-cloud environments

Cloud Computing emerged as a disruptive technology for managing IT services over the internet, evolving from Grid Computing, Utility Computing and Software-as-a-Service (SaaS). After an initial scepticism, international companies are widely migrating their IT workload on private and public Cloud to optimise geographical coverage and launch new digital services. Currently, hybrid and Multi-Cloud environments introduce additional complexity in managing the Quality of Services (QoS) therefore requiring more sophisticated Service Level Agreements (SLAs). To deal with these issues, our research developed a SLA-aware Lean Information Service Architecture (LISA) for managing Multi-Cloud environments that support users in the whole service lifecycle. LISA performance was tested in the Innovation Lab of a global telco operator, by deploying services on private Cloud and public Cloud providers. Experimental results prove not only LISA's effectiveness but also efficiency in various aspects, such as preventing SLA violations and service performance degradations, optimising the QoS, and controlling the service components deployed across multiple public Cloud providers.
Learning-free pattern detection for manuscript research:

Abstract Automatic pattern detection has become increasingly important for scholars in the humanities as the number of manuscripts that have been digitised has grown. Most of the state-of-the-art methods used for pattern detection depend on the availability of a large number of training samples, which are typically not available in the humanities as they involve tedious manual annotation by researchers (e.g. marking the location and size of words, drawings, seals and so on). This makes the applicability of such methods very limited within the field of manuscript research. We propose a learning-free approach based on a state-of-the-art Naïve Bayes Nearest-Neighbour classifier for the task of pattern detection in manuscript images. The method has already been successfully applied to an actual research question from South Asian studies about palm-leaf manuscripts. Furthermore, state-of-the-art results have been achieved on two extremely challenging datasets, namely the AMADI_LontarSet dataset of handwriting on palm leaves for word-spotting and the DocExplore dataset of medieval manuscripts for pattern detection. A performance analysis is provided as well in order to facilitate later comparisons by other researchers. Finally, an easy-to-use implementation of the proposed method is developed as a software tool and made freely available.
Societal Biases in Language Generation: Progress and Challenges

Technology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to communicate in a natural manner. While techniques can effectively generate fluent text, they can also produce undesirable societal biases that can have a disproportionately negative impact on marginalized populations. Language generation presents unique challenges for biases in terms of direct user interaction and the structure of decoding techniques. To better understand these challenges, we present a survey on societal biases in language generation, focusing on how data and techniques contribute to biases and progress towards reducing biases. Motivated by a lack of studies on biases from decoding techniques, we also conduct experiments to quantify the effects of these techniques. By further discussing general trends and open challenges, we call to attention promising directions for research and the importance of fairness and inclusivity considerations for language generation applications.
An integrated data mining approach to predict electrical energy consumption

This study proposes an integrated adaptive neuro fuzzy inference system (ANFIS) and gene expression programming (GEP) approach to predict long-term electrical energy consumption. The developed hybrid method uses ANFIS to find parameters with maximum effect on the electricity demand. Thereafter, the GEP algorithm is deployed to derive a robust mathematical model for the prediction of the electricity demand. Various statistical criteria are considered to verify the validity of the model. The predictions made by the ANFIS-GEP model are compared with those obtained by the simple GEP and hybrid artificial neural network (ANN)-ANFIS methods. The proposed ANFIS-GEP technique is more computationally efficient and accurate than GEP, and notably outperforms ANFIS-ANN.
Dynamic Ad Hoc Clock Synchronization

Clock synchronization allows parties to establish a common notion of global time by leveraging a weaker synchrony assumption, i.e., local clocks with approximately the same speed. Despite intensive investigation of the problem in the fault-tolerant distributed computing literature, existing solutions do not apply to settings where participation is unknown, e.g., the ad hoc model of Beimel et al. [EUROCRYPT 17], or is dynamically shifting over time, e.g., the fluctuating/sleepy/dynamic-availability models of Garay et al. [CRYPTO 17], Pass and Shi [ASIACRYPT 17] and Badertscher et al. [CCS 18]. We show how to apply and extend ideas from the blockchain literature to devise synchronizers that work in such dynamic ad hoc settings and tolerate corrupted minorities under the standard assumption that local clocks advance at approximately the same speed. We discuss both the setting of honest-majority hashing power and that of a PKI with honest majority. Our main result is a synchronizer that is directly integrated with a new proof-of-stake (PoS) blockchain protocol, Ouroboros Chronos, which we construct and prove secure; to our knowledge, this is the first PoS blockchain protocol to rely only on local clocks, while tolerating worst-case corruption and dynamically fluctuating participation. We believe that this result might be of independent interest.
Risk Philosophy of Education Digitalization: Cognitive Risks and Ways to Create a Secure Communicative Educational Environment

The instability of modern civilizational development and the growing risks of constructive socio-anthropological dynamics pose new challenges for philosophical knowledge. The article is devoted to the systemic problem of digitalization of education and the understanding of cognitive risks. The research methodology is based on philosophical, psychological, pedagogical and information technology approaches to the process of digitalization of education, determining the nature and forms of manifestations of cognitive risks. The authors rely on numerous foreign and domestic concepts of informatization (digitalization) of education and focus on several types of risks of the cognitive sphere of students: information over-saturation, devaluation of memory capabilities, reduction in the level of critical thinking. The article emphasizes the probabilistic nature of these cognitive risks, defined by a set of scientific, methodological, organizational, methodological, psychological and pedagogical factors and individual personality, psychophysiological characteristics of students who are vulnerable to these risks to different degrees. The result of the study is a proposal on ways to level the cognitive risks of digitalization of education. The authors call for a balanced strategy and the consolidated work of all subjects of the education system in order to achieve the formation of a safe communicative and educational environment.
2D Deep Video Capsule Network with Temporal Shift for Action Recognition

Action recognition in continuous video streams is a growing field since the past few years. Deep learning techniques and in particular Convolutional Neural Networks (CNNs) achieved good results in this topic. However, intrinsic CNNs limitations begin to cap the results since 2D CNN cannot capture temporal information and 3D CNN are to much resource demanding for real-time applications. Capsule Network, evolution of CNN, already proves its interesting benefits on small and low informational datasets like MNIST but yet its true potential has not emerged. In this paper we tackle the action recognition problem by proposing a new architecture combining Temporal Shift module over deep Capsule Network. Temporal Shift module permits us to insert temporal information over 2D Capsule Network with a zero computational cost to conserve the lightness of 2D capsules and their ability to connect spatial features. Our proposed approach outperforms or brings near state-of-the-art results on color and depth information on public datasets like First Person Hand Action and DHG 14/28 with a number of parameters 10 to 40 times less than existing approaches.
Urgensi Pelatihan KTI untuk Meningkatkan Kompetensi Widyaiswara dalam Penulisan Karya Ilmiah

The objectives of this research are as follows: 1) The skills of the Jambi Bapelkes widyaiswara in making scientific papers, 2) The constraints of the Jambi Bapelkes Widyaiswara in making scientific papers, 3) The efforts made by the Jambi Bapelkes widyaiswara in improving the ability to make scientific papers. This study uses a qualitative approach with a phenomenological design. Methods of data collection using interviews, observation, and documentation study. The data validity technique used source and technique triangulation. While the data analysis technique uses an interactive model with the process of data collection, data reduction, data presentation and conclusions. The results of this study can be seen as follows: First, the skills of lecturers in writing KTI are sufficient, although there are several aspects that must be improved, Second, some of Widyaiswari's obstacles in compiling KTI are related to opportunities, book research, limited supporting facilities and lack of motivation. , ketiga, Some of the efforts made to improve skills in preparing KTI are through: training of trainers, training, participating in webinars, work shops, a lot of literacy on the results of previous research, and following tutorial activities on YouTube.
GestureMap: Supporting Visual Analytics and Quantitative Analysis of Motion Elicitation Data by Learning 2D Embeddings

This paper presents GestureMap, a visual analytics tool for gesture elicitation which directly visualises the space of gestures. Concretely, a Variational Autoencoder embeds gestures recorded as 3D skeletons on an interactive 2D map. GestureMap further integrates three computational capabilities to connect exploration to quantitative measures: Leveraging DTW Barycenter Averaging (DBA), we compute average gestures to 1) represent gesture groups at a glance; 2) compute a new consensus measure (variance around average gesture); and 3) cluster gestures with k-means. We evaluate GestureMap and its concepts with eight experts and an in-depth analysis of published data. Our findings show how GestureMap facilitates exploring large datasets and helps researchers to gain a visual understanding of elicited gesture spaces. It further opens new directions, such as comparing elicitations across studies. We discuss implications for elicitation studies and research, and opportunities to extend our approach to additional tasks in gesture elicitation.
LSTM-Based Jamming Detection for Satellite Communication with Alpha-Stable Noise

In this paper, we propose an intelligent detection method for satellite communication jamming based on the long and short-term memory (LSTM) networks. The LSTM network can effectively predict the value of the signal at a particular moment. Specifically, a myriad filter is used to suppress the alpha-stable noise in the receiving signal to guarantee that the suppressed noise follows the Gaussian distribution approximately. For the LSTM network training, a priori noise-free jamming signal is used to ensure that the network has a specific memory of the jamming signal. Then, we utilize the trained LSTM network for satellite communication jamming detection. Furthermore, the mean square error between the predicted value and the actual value is examined as the test statistic. It is observed that the mean square error with the only input of noise is larger than that with the inputs of both signal and noise. In addition, a threshold is obtained by recording the noise-only signal into the network multiple times. Consequently, the detection of jamming signals is achieved based on the proposed model. Simulation results show that the detection rate of the satellite communication jamming can reach 98% when the jamming-to-noise ratio is 7dB.
A rigorous introduction to linear models

This book is meant to provide an introduction to linear models and the theories behind them. Our goal is to give a rigorous introduction to the readers with prior exposure to ordinary least squares. In machine learning, the output is usually a nonlinear function of the input. Deep learning even aims to find a nonlinear dependence with many layers, which require a large amount of computation. However, most of these algorithms build upon simple linear models. We then describe linear models from different perspectives and find the properties and theories behind the models. The linear model is the main technique in regression problems, and the primary tool for it is the least squares approximation, which minimizes a sum of squared errors. This is a natural choice when we're interested in finding the regression function which minimizes the corresponding expected squared error. This book is primarily a summary of purpose, significance of important theories behind linear models, e.g., distribution theory and the minimum variance estimator. We first describe ordinary least squares from three different points of view, upon which we disturb the model with random noise and Gaussian noise. Through Gaussian noise, the model gives rise to the likelihood so that we introduce a maximum likelihood estimator. It also develops some distribution theories via this Gaussian disturbance. The distribution theory of least squares will help us answer various questions and introduce related applications. We then prove least squares is the best unbiased linear model in the sense of mean squared error, and most importantly, it actually approaches the theoretical limit. We end up with linear models with the Bayesian approach and beyond.
Quantum computing hardware in the cloud: Should a computational chemist care?

Abstract Within the last decade much progress has been made in the experimental realization of quantum computing hardware based on a variety of physical systems. Rapid progress has been fuelled by the conviction that sufficiently powerful quantum machines will herald enormous computational advantages in many fields, including chemical research. A quantum computer capable of simulating the electronic structures of complex molecules would be a game changer for the design of new drugs and materials. Given the potential implications of this technology, there is a need within the chemistry community to keep abreast with the latest developments as well as becoming involved in experimentation with quantum prototypes. To facilitate this, here we review the types of quantum computing hardware that have been made available to the public through cloud services. We focus on three architectures, namely superconductors, trapped ions and semiconductors. For each one we summarize the basic physical operations, requirements and performance. We discuss to what extent each system has been used for molecular chemistry problems and highlight the most pressing hardware issues to be solved for a chemistry‐relevant quantum advantage to eventually emerge.
Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy

We propose a Bayesian pseudo posterior mechanism to generate record-level synthetic databases equipped with an $(ε,δ)-$ probabilistic differential privacy (pDP) guarantee, where $δ$ denotes the probability that any observed database exceeds $ε$. The pseudo posterior mechanism employs a data record-indexed, risk-based weight vector with weight values $\in [0, 1]$ that surgically downweight the likelihood contributions for high-risk records for model estimation and the generation of record-level synthetic data for public release. The pseudo posterior synthesizer constructs a weight for each data record using the Lipschitz bound for that record under a log-pseudo likelihood utility function that generalizes the exponential mechanism (EM) used to construct a formally private data generating mechanism. By selecting weights to remove likelihood contributions with non-finite log-likelihood values, we guarantee a finite local privacy guarantee for our pseudo posterior mechanism at every sample size. Our results may be applied to \emph{any} synthesizing model envisioned by the data disseminator in a computationally tractable way that only involves estimation of a pseudo posterior distribution for parameters, $θ$, unlike recent approaches that use naturally-bounded utility functions implemented through the EM. We specify mild conditions that guarantee the asymptotic contraction of $δ$ to $0$ over the space of databases. We illustrate our pseudo posterior mechanism on the sensitive family income variable from the Consumer Expenditure Surveys database published by the U.S. Bureau of Labor Statistics. We show that utility is better preserved in the synthetic data for our pseudo posterior mechanism as compared to the EM, both estimated using the same non-private synthesizer, due to our use of targeted downweighting.
Exploiting Forwardness: Satisfiability and Query-Entailment in Forward Guarded Fragment

We study the complexity of two standard reasoning problems for Forward Guarded Logic (\(\mathcal {FGF}\)), obtained as a restriction of the Guarded Fragment in which variables appear in atoms only in the order of their quantification. We show that \(\mathcal {FGF}\) enjoys the higher-arity-forest-model property, which results in \(\textsc {ExpTime}\)-completeness of its (finite and unrestricted) knowledge-base satisfiability problem. Moreover, we show that \(\mathcal {FGF}\) is well-suited for knowledge representation. By employing a generalisation of Lutz's spoiler technique, we prove that the conjunctive query entailment problem for \(\mathcal {FGF}\) remains in \(\textsc {ExpTime}\).