Reviewerly/exHarmony huggingface dataset:
Blockwise Temporal-Spatial Pathway Network

Algorithms for video action recognition should consider not only spatial information but also temporal relations, which remains challenging. We propose a 3D-CNN-based action recognition model, called the blockwise temporal-spatial path-way network (BTSNet), which can adjust the temporal and spatial receptive fields by multiple pathways. We designed a novel model inspired by an adaptive kernel selection-based model, which is an architecture for effective feature encoding that adaptively chooses spatial receptive fields for image recognition. Expanding this approach to the temporal domain, our model extracts temporal and channel-wise attention and fuses information on various candidate operations. For evaluation, we tested our proposed model on UCF-101, HMDB-51, SVW, and Epic-Kitchen datasets and showed that it generalized well without pretraining. BTSNet also provides interpretable visualization based on spatiotemporal channel-wise attention. We confirm that the blockwise temporal-spatial pathway supports a better representation for 3D convolutional blocks based on this visualization.
Cyclic Diffeomorphic Transformer Nets For Contour Alignment

Shape analysis is a key task in image processing. A common method for representing a 2D shape is via a polygon, where the latter is a discretized version of the contour outlining the shape. However, due to the problem of curve reparameterization (i. e., points “sliding” along the contour), even if several shapes are very similar, their representations might be misleadingly far from each other. This misalignment problem confounds shape analysis. As a remedy, we propose a deep-learning framework, based on the recently-proposed diffeomorphic transformers nets. The proposed method handles either a single class (in an unsupervised manner) or multiple classes (in a semi-supervised manner), and is amenable to the warp-around effect exhibited in closed contours. Moreover, unlike typical alignment methods unrelated to learning, the proposed method aligns not only the original (“training”) shapes but also generalizes to test shapes (even if no class labels are given during the test). Our code is publicly available at https://git;hub.com/BGU-CS-VIL/CDTNCA.
Clinically Guided Trainable Soft Attention for Early Detection of Oral Cancer

Oral cancer disproportionately affects low- and middle-income countries, where a lack of access to appropriate medical care contributes towards late disease presentation. Using artificial intelligence to facilitate the automated identification of high-risk oral lesions can improve patient survival rates. With image classification using oral cavity images and other forms of medical images, the information to be classified can often be extremely localized. To address this problem, we propose the use of convolutional neural networks with trainable soft attention. Further to this, we incorporate the use of localization loss to penalize the difference between attention maps and clinically annotated mask. This effectively allows clinicians to help guide soft attention. Improvements to the baseline were made, with an accuracy of 0.8333 and a ROC AUC of 0.8632, which equates to increases of 0.0245 and 0.0394, respectively. This accuracy corresponds to a sensitivity of 0.8469 and a specificity of 0.8208. Perhaps of more importance, is a model that demonstrates better capability at paying attention to the lesions in its decision making. Furthermore, visualizing resulting attention maps can help to strengthen clinical confidence in AI decision making.
Analysis of the Level of Diversification (Variability) of Regional Systems of Secondary Vocational Education

The article analyzes the willingness of regional educational systems to provide, and the potential students themselves to acquire, professions and specialties in educational programs of secondary vocational education through the prism of the variability of oﬀered / acquired professions and specialties.
Heuristic Model to Compute Indices for Classification of Incidence and Non-Incidence of Thunderstorms Over Ranchi With Atmospheric Parameter

Prediction of incidences of thunderstorms using different techniques is quite well establsihed. In this paper, a heuristic equation is proposed which relates the correlation coefficient of atmospheric parameters with the number of hourly incidences of thunderstorms. There are four ways to compute the indices values from the proposed heuristic equation. These indices values are used in the classification of the hourly incidences of thunderstorms. The proposed equation and indices work well, as tested on 2018 pre-monsoon hourly atmospheric data and validated with April 2019 and 2020 hourly data. From four indices, the first index value is computed with normalized average values of parameters of only hourly incidence of thunderstorms data of 2016-2017 in the month-wise method. The other three indices use optimization techniques namely, the Teaching Learning Based Optimization (TLBO) technique, Differential Evaluation (DE), and Simulated Annealing (SA). TLBO shows the better classification of hourly incidence of thunderstorms for 2018 atmospheric data. TLBO is also precisely validating the hourly incidences of thunderstorms for April 2019 and April 2020 hourly atmospheric data. It performed better with 2020 data by 88%. The variations of atmospheric parameters before, after or, during the incidences of thunderstorms are also depicted.
Automatic Security Inspection Framework for Trustworthy Supply Chain

Threats and risks against supply chains are increasing and a framework to add the trustworthiness of supply chain has been considered. In this framework, organisations in the supply chain validate the conformance to the pre-defined requirements. The results of validations are linked each other to achieve the trustworthiness of the entire supply chain. In this paper, we further consider this framework for data supply chains. First, we implement the framework and evaluate the performance. The evaluation shows 500 digital evidences (logs) can be checked in 0.28 second. We also propose five methods to improve the performance as well as five new functionalities to improve usability. With these functionalities, the framework also supports maintaining the certificate chain.
Face Texture Generation And Identity-Preserving Rectification

Textures are a vital asset in conveying a realistic impression of a 3D scene to the viewers. In order to obtain high-quality textures, real-life objects are scanned or designers create handcrafted textures. Both tasks involve manual work, are quite time-consuming, and therefore fail when a large quantity of textures is required. Thus, we propose to use a Generative Adversarial Network to generate an artificial texture. As textures need to be perfectly aligned with the 2D projection of the 3D model, our method involves a texture rectification technique, ensuring that the generated textures wrap well onto the 3D model. On the example of face textures, we illustrate that our method generates textures of high quality and variance. Moreover, we show that the rectification process preserves the facial appearance and identity, indicating that we successfully disentangle features responsible for facial appearance and the texture’s fit.
Research on English translation of Chinese College Students Based on Computer Scoring System

Abstract With the development of international economic situation and the expansion of international trade, the learning of English, an international common language, has been paid more and more attention. In college students’ English learning, translation practice is an essential part. English translation scoring has gradually changed from full manual scoring to semi manual scoring and computer intelligence scoring. This paper focuses on the computer scoring system on College Students’ English translation needs and methods to make a summary.
Hybrid Pruning And Sparsification

A hybrid approach based on the combination of saliency-based neural pruning and regularization-based sparsification is proposed. We propose using a graph diffusion process for determining the neuron importance for pruning. Then, we use a regularization loss based on weighted $L_{1}-$norm and $L_{2}-$norm during fine-tuning to recover the lost performance. This is followed by a threshold step to further impose sparsification. We demonstrate such a hybrid approach achieves significantly better performance in comparison to purely regularization-based sparsification for large neural networks. To this end, we assessed our proposed method on three tasks, including: image classification (3 network architectures), audio classification and image compression.
A Study of the Correlation of Metafeatures Used for Metalearning

Metalearning has been largely used over the last years to recommend machine learning algorithms for new problems based on past experience. For such, the first step is the creation of metabase, or metadataset, containing metafeatures extracted from several datasets along with the performance of a pool of candidate algorithm(s). The next step is the induction of machine learning metamodels using the metabase as input. These models can recommend the most suitable algorithms for new datasets based on their metafeatures values. An effective metalearning system must employ metafeatures that characterize essential aspects of the datasets while also distinguishing different problems and solutions. The characterization process should also show a low computational cost, otherwise, the recommendation system can be replaced by a standard trial-and-error approach. This paper proposes the use of an unsupervised correlation-based feature selection strategy to identify a reduced subset of metafeatures for metalearning systems. Empirically, the predictive performance achieved by metalearning systems using the subset of selected metafeatures is similar or better than the performance obtained using the whole set of metafeatures. In addition, a noteworthy reduction in the number of metafeatures needed is observed, implying computational cost reductions.
Effective low capacity status prediction for cloud systems

In cloud systems, an accurate capacity planning is very important for cloud provider to improve service availability. Traditional methods simply predicting "when the available resources is exhausted" are not effective due to customer demand fragmentation and platform allocation constraints. In this paper, we propose a novel prediction approach which proactively predicts the level of resource allocation failures from the perspective of low capacity status. By jointly considering the data from different sources in both time series form and static form, the proposed approach can make accurate LCS predictions in a complex and dynamic cloud environment, and thereby improve the service availability of cloud systems. The proposed approach is evaluated by real-world datasets collected from a large scale public cloud platform, and the results confirm its effectiveness.
A note on computing with Kolmogorov Superpositions without iterations

We extend Kolmogorov's Superpositions to approximating arbitrary continuous functions with a noniterative approach that can be used by any neural network that uses these superpositions. Our approximation algorithm uses a modified dimension reducing function that allows for an increased number of summands to achieve an error bound commensurate with that of r iterations for any r. This new variant of Kolmogorov's Superpositions improves upon the original parallelism inherent in them by performing highly distributed parallel computations without synchronization. We note that this approach makes implementation much easier and more efficient on networks of modern parallel hardware, and thus makes it a more practical tool.
An Automatic Verification Method of Intelligent Substation Operation Order Based on Chinese Semantic Deep Learning

Verifying the intelligent substation Chinese operation order is an important step to ensure the accuracy of substation operations. The current manual verification method based on experience has strong subjectivity, low efficiency and low reliability. In view of this, this paper proposes an automatic verification method of operation order based on the Chinese semantic deep learning model: Global vectors for word representation-convolution Recurrent Neural Network (Glove-RCNN). By combining the text vectorization model Glove and text mining model RCNN, this method is feasible in realizing the semantic discrimination and automatic verification of correctness and error of the operation order texts. The experimental result shows that the MicroF <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">score</inf> and MacroF <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">score</inf> of the operation order verification model is 93.98% and 94.19%, which can accurately judge the correctness of the operation order, so as to effectively improve the verification efficiency.
Application Research of Speech Instruction Recognition in Human-computer Interaction

Speech instruction recognition is an important research direction in the field of human and intelligent machine interaction. With the development of deep learning, more and more researchers pay attention to the automatic speech recognition technology based on convolutional neural network. By establishing an end-to-end speech recognition model based on deep neural network, taking logarithmic amplitude spectrum features as the network input, using connectionist temporal classifiers to carry out time sequence classification, and using convolutional neural network to deal with the correlation between frames, an end-to-end speech recognition system is implemented. On this basis, taking the speech interaction scene of intelligent home appliances in the living room as an example, the speech control instruction database of smart home appliances is constructed, and the application research of speech instruction recognition and data instruction generation is carried out. The method of transfer learning is adopted. We train a pre-training model with open source data set firstly, and then re-train the model with self-recorded speech control instruction database. The experimental results show that the proposed algorithm achieves 12.31% WER on public AISHELL-1 dataset and 2.1% WER on our self-recorded speech control instruction database. The speech instruction recognition system realized in this paper can quickly recognize speech instructions and convert them into formatted data instructions that can drive intelligent home appliances, thus improving the communication efficiency between human and machine.
Investigating documented information for accurate effort estimation in agile software development

An Agile development team estimates the effort of a work item to plan a sprint (i.e., an iteration in Scrum). Hence, reliable effort estimation would help the team create a reliable sprint plan. Prior studies proposed automated approaches to help the team to estimate the effort accurately. However, the effort estimated by the previously proposed approaches may become inaccurate when the related information is changed. Especially when the estimated effort is changed after the sprint has been planned, the sprint plan may be invalidated and the team might waste their time and effort spent in planning. This thesis aims to help the Agile development team improve the stability of effort estimation (in the Story Points unit) while aligning with the just-in-time practice of Agile. Hence, we first conduct empirical studies using mixed-methods approaches to investigate the potential impact of instability of Story Points. To help an Agile team to achieve reliable effort estimation with optimal effort, we will develop approaches to predict the future Story Points changes and the future information changes to help the team cope with uncertainty when finalizing the sprint plan.
The use of simulation and collaborative tools in teaching management to engineering students

The current pandemic crisis has brought up the need to use digital and distance learning tools to ensure the involvement of students and their participation in classes and academic work. In this article we present the tools used in the Management course at Instituto Superior Técnico involving, namely, simulation through management games, clickers and collaborative techniques useful for online lectures and with potential to help with engaging and providing feedback when managing a large number of students. The success of the application of these online methods is then evaluated and discussed, both under the teachers' perspective and under the students' perspective, the latter resorting to an online survey. Although before the pandemic the game ISTMC and the clickers were already used, these and other tools were used with higher impact in the last two years. The conclusions point out to very positive experiences with the online lecturing and to the possibility of further use of some of these in a blended online-classroom situation.
Learning causal structures using hidden compact representation

Learning causal structures from observational data is of great challenge and interest in many disciplines. Various methods have demonstrated their effectiveness for causal structure learning on different types of data, but most of them are suffered from the indeterminacy of Markov equivalence classes, especially on discrete data. A recent breakthrough formulates the problem as finding a hidden compact representation (HCR) with lower cardinality to distinguish the correct causal direction. However, method based on the HCR model is only applicable in bivariate cases. In this paper, we generalize the HCR model to multivariate cases and provide an effective likelihood-based algorithm for causal structure learning in presence of hidden compact representation. Our theoretical results show that under some weak technique conditions about the underlying causal mechanism, causal directions are still identifiable even in multivariate cases. Our empirical studies demonstrate the effectiveness of proposed methods on various types of data and structures.
IoT Technology and Supply Chain Management

This article presents the supply chain management applications in economics but especially in the transportation industry. The methodology proposed in this article is based on the use of ledger technology for the integration of transport sensor systems. Ledger technology integration methods are applied, and their results contribute to measuring performance in traffic management. Centered on the development of the processes realized by the authors, a methodology - was developed within the Department of Intelligent Transport Systems of UPB, for the integration of sensors from ITS.The paper offers a concrete solution obtained at the department level. This solution based on the implementation of the ledger technology algorithm represents a new approach for monitoring and transport planning in accordance with ITS requirements.The practical aspects approached by the authors are related to the identification of the obstacles encountered in the implementation of urban transport planning management using ledger technology.Methodology based on the supply chain management algorithms approach for monitoring and predicting traffic planning using IoT networks in ITS.
Interactive Clothes Image Retrieval via Multi-modal Feature Fusion of Image Representation and Natural Language Feedback

Clothes image retrieval is an element task that has attracted research interests during the past decades. While in most case that single retrieval cannot achieve the best retrieval performance, we consider to develop an interactive image retrieval system for fashion outfit search, where we utilize the natural language feedback provided by the user to grasp compound and more specific details for clothes attributes. In detail, our model is divided into two parts: feature fusion part and similarity metric learning part. The fusion module is used for combining the feature vectors of the modified description and the feature vectors of the image part. It is then optimized in an end-to-end method via a matching objective, where we have adopted contractive learning strategy to learn the similarity metric. Extensive simulations have been conducted. The simulation results show that the compared with other complex multi-model proposed in recent years, our work improves the model performance while keeping the model simple in architecture.
Non-Verbal Communication

M. Wiener et al. called their article “Nonverbal behaviour and non-verbal communication”, and the distinction is important: not all non-verbal behaviour can be regarded as communicative. The term communication also poses problems of definition, particularly with respect to what behaviours can be properly regarded as communicative. Wiener et al. also challenge the view that the only non-verbal behaviours that can be regarded as communicative are those that are intended as such. Indeed, non-verbal communication may take place even against the express intentions of the encoder. Non-verbal cues can be said to communicate information about emotion, speech, individual differences, and interpersonal relationships; their significance also needs to be considered in specific social contexts. The central importance of non-verbal cues in the communication of emotion has led some writers to regard body movement as an alternative system to speech, offering a more reliable indicator of people’s true feelings.
Generating Thermal Human Faces for Physiological Assessment using Thermal Sensor Auxiliary Labels

Thermal images reveal medically important physiological information about human stress, signs of inflammation, and emotional mood that cannot be seen on visible images. Providing a method to generate thermal faces from visible images would be highly valuable for the telemedicine community in order to show this medical information. To the best of our knowledge, there are limited works on visible-to-thermal (VT) face translation, and many current works go the opposite direction to generate visible faces from thermal surveillance images (TV) for law enforcement applications. As a result, we introduce favtGAN, a VT GAN which uses the pix2pix image translation model with an auxiliary sensor label prediction network for generating thermal faces from visible images. Since most TV methods are trained on only one data source drawn from one thermal sensor, we combine datasets from faces and cityscapes. These combined data are captured from similar sensors in order to bootstrap the training and transfer learning task, especially valuable because visible-thermal face datasets are limited. Experiments on these combined datasets show that favtGAN demonstrates an increase in SSIM and PSNR scores of generated thermal faces, compared to training on a single face dataset alone.
Detection of moving objects using adaptive multi-feature histograms

This paper presents a moving object detection scheme that incorporates three innovations. First, considering the inter-frame consistency of pixels, we extend the compact binary face descriptor (CBFD) to the temporal domain and propose a novel local binary descriptor named temporally-consistent local compact binary descriptor (TC-LCBD), which exploits the useful correlation of the intensities of inter-frame pixels to guarantee good performance in complex scenes. We do this mainly because the background scene between frames has a significant coherence. Second, both color and TC-LCBD features are modeled as a group of adaptive histograms for characterizing each pixel, which can enhance the robustness to dynamic backgrounds and illumination changes. Third, by comparing changes in histogram proximity between two adjacent frames, we can dynamically adjust the model sensitivity and adaptation rate without user intervention. Experimental results on well-known, challenging data sets demonstrate that the proposed method significantly outperforms many state-of-the-art methods.