Reviewerly/exHarmony huggingface dataset:
Fuzzy Spatio-Temporal Data Analysis on Disease Outbreaks and Air Transportation

A challenging problem for human societies that affects human life is the spread of diseases such as COVID-19. Travel, especially air transportation, plays a significant role in the spread of such diseases. Analysis of air traffic data and its interaction with the disease outbreak is one of the applied and research topics. Flights from infected areas to less infected regions increase the outbreak. On the other hand, by following warnings and policies, air travel declines in such periods. The effects are reflected in the collected data with a time gap that requires efficient methods for rich analysis. This aspect is overlooked in existing research. In this paper, a new approach is proposed for spatio-temporal analysis that is based on granular computing and fuses the air traffic data with COVID-19 statistics. Data in diverse sources may be at different levels of granularity. Therefore, analyzing the interaction of two phenomena extracted from different sources is a challenging issue. Moreover, there may not be an exact match in other data sources for some data items, but applying a fuzzy inference system and constructing enriched data granules by using geographical knowledge bases eliminates this shortcoming. The proposed method fills the gaps and adjusts the differences to discover new patterns that other methods cannot detect.
On the positive experience of using the possibilities of the explosive laboratory of the Forensic Center of the Main Directorate of the Ministry of Internal Affairs of Russia in St. Petersburg and the Leningrad Region in terrorist crimes detection and investigation

This article is devoted to the description of the still functioning explosive laboratory created in the structure of the Expert and Technical Department of the Criminal Militia Service of the Central Government of St. Petersburg and the Leningrad Region. The main tasks carried out by the employees of the 10th department of the State Economic Department of the Ministry of Internal Affairs of Russia in St. Petersburg and the Leningrad Region (department of explosive and fire and technical expertise) are highlighted. Special attention is paid to the participation of these employees in crime scenes investigation of offences involving using explosive objects with subsequent explosive expertise. The author provides examples of high-profile criminal cases. In addition, the author underlines the significant role of interaction of the laboratory under consideration with various law enforcement services for highly efficient expert support in the investigation of crimes related to the illicit trafficking of explosive objects, as well as in solving complex and far from secure operational tasks in order to further scientific and practical development for needs of law enforcement agencies (emphasis is placed on scientific researches of the employees of the 10th department of the Forensic Center).
Enhancing Robustness of Classifiers Based on PCA

To date, deep learning techniques have been widely used. However, deep neural networks (DNNs) are vulnerable to adversarial attacks, which has become one of the hidden risks issues affecting system security. The adversarial sample is a perturbation input to fool the deep learning model. The inherent weakness of DNNs that lacks robustness to adversarial samples brings security problems, especially for tasks that require high reliability. This paper proposed a robustness enhancing method based on principal component analysis (PCA) and applied it to deep networks, which enhanced the ability of DNNs to resist adversarial attacks. Specifically, the proposed method firstly used PCA to downscale the clean samples, and then, chose two non-target attacks, DeepFool and FGSM, to craft adversarial samples pre-and-post downscale. Finally, by evaluating the changes in the robustness of the classifier, we draw the corresponding analytical conclusions. Experimental results on MNIST show that the proposed method makes deep networks more robust against white-box attacks.
EDNet: Encoder-Decoder Network with Low Rank and Margin Regularizations for Saliency Detection

Deep learning based methods have achieved great progress in the saliency detection domain. However, it remains to be challenging to segment objects in complicated scenarios consistently and accurately. To address the problem, we propose an encoder-decoder based framework in this paper, where the encoder extracts deep features for each super-pixel via fast R-CNN and attention mechanism, and a loss term is introduced to penalize the segmentation inaccuracy; the decoder makes saliency predictions by two kinds of bidirectional long short-term memory networks: the segmented BLSTM collects information horizontally and the skipping BLSTM fuse them along the vertical direction. For further improving the detection accuracy and robustness, low rank constraint is imposed on the background super-pixels, and the classification margin between salient and non-salient regions is expanded simultaneously. In experiments, we evaluate the networks on five benchmark datasets, and the results under different metrics demonstrate that our model without any post-processing has superiority over other state-of-the-art ones.
A Logical Autobiography

I was born in Auchtermuchty, a small village in Fife, Scotland, the son of a lowland mother, Meta Mowat, daughter of a country headmaster, and a highland father, William Urquhart from Lairg, Sutherland. (Auchtermuchty appears as the fictional village of Tannochbrae in the 1990s television series Doctor Finlay.) The family business in Lairg was a butcher’s shop, known as Urquhart the Butcher, even for some years after my father’s family had given it up.
Benchmarking Analysis of CNN Architectures for Artificial Intelligence Platforms

The prompt innovations in Digital Technologies with the availability of credible data have led to the emergence of an era of Artificial Intelligence and Deep Learning. This demonstrates their effectiveness in solving complex problems, particularly in image classification and object recognition applications, with the assistance of Convolutional Neural Networks (CNNs). However, such algorithms need to be executed within a certain time frame specifically applications like High Performance Computing (HPC), Autonomous vehicles, Gaming etc. The requirement of time certainty brings hardware accelerators into the picture. These hardware accelerators not only accelerate time-critical tasks but have also been proven effective in enhancing the throughput of CNNs. In this research, we have tried to tune in performances of different CNN models i.e. Alexnet, SqueezeNet1.1, GoogleNet-v1, and VGG-16 of the Caffe framework which have been pre-trained and converged, using the Bench-Marking and Cross Check Tools of OpenVINO Toolkit. The tool define the performance of CNN Models based on latency, throughput, absolute and relative differences in each layer of these models. The CNN models have been simulated on platforms like Intel i5-8265 CPU, 1.60 Ghz and Integrated GPU UHD graphics 620 using OpenVINO Toolkit, which helped in running the simulations on Windows 10. Furthermore, this study is expected to direct the future development of an efficient accelerator on specialized hardware accelerators and also be useful for deep learning researchers.
A High-Speed Neural Architecture Search Considering the Number of Weights

Neural architecture search (NAS) is a promising method to ascertain network architecture automatically and to build a suitable network for a particular application without any human intervention. However, NAS requires huge computation resources to find the optimal parameters of a network in the training phase of each search. Because a trade-off generally exists between model size and accuracy in deep learning models, the model size tends to increase in pursuit of higher accuracy. In applications with limited resources, such as edge AI, reducing the network weight might be more important than improving its accuracy. Alternatively, achieving high accuracy with maximum resources might be more important. The objective of this research is to find a model with sufficient accuracy with a limited number of weights and to reduce the search time. We improve the Differentiable Network Search (DARTS) algorithm, one of the fastest NAS methods, by adding another constraint to the loss function, which limits the number of network weights. We evaluate the proposed algorithm using three constraints. Compared to the conventional DARTS algorithm, the proposed algorithm reduces the search time by up to 40% when the model size range is set properly. It achieves comparable accuracy with that of DARTS.
Assessing virtual and on-site technical trainings during the first year of covid-19 pandemic

In 2020, Covid-19 pandemic firstly hit many countries including Indonesia. Since then, the methods on disseminating the agricultural technology have evolved accordingly. This paper assessed nine series of virtual and on-site technical trainings (or the so-called Bimtek in this paper) carried out from February to November 2020 in nine sub-districts from five provinces. Zoom application was used to deliver the materials during virtual Bimtek. Whilst, on-site training applied such methods: meeting in the classroom and field visit to practice the technology. The participants were diverse, among others: farmers and millennial farmers, extension officers, lecturers, college students and community (551 in total). The nature of participants depended on the type of Bimtek. Results showed that both Bimtek had the range of value between 70-85% in term of materials to meet the needs, materials to be applied, motivation and technical assistance from resource person. However, the percentage of knowledge change in virtual was higher than that in on-site Bimtek. Initial assessment showed that participants had good knowledge in remembering the technology, even some had also applied the technology in their farming practice. Thus, both Bimtek could improve farmers’ knowledge, from which the intention to change the farming practice could be persuaded.
Cross-domain Person Re-identification Based on the Sample Relation Guidance

The existing mainstream cross-domain person re-identification (Re-ID) methods mainly focus on reducing the deviation of the generated pseudo labels, and they did not introduce veracious label information for algorithm training on the unlabeled target domain. In this paper, we propose a new sample relation guidance (SRG) method. Specifically, the sample relation is a real label, which represents a definite positive sample pairs’ relation or negative sample pairs’ relation. Here, we construct a triple-branch network to form sample relation labels to improve the expressive power of features. In addition, the potential relationship of target domain label loss and source domain label loss is explored, and an adaptive adjustment label loss (ADLL) method is proposed, which effectively improves the generalization performance of the model. Extensive experiments over three benchmarks proved that our method outperforms the state-of-the-art methods.
On the Finite Model Property of Weak Intuitionistic Tense Logic

In this paper, we study the finite model property of weak intuitionistic tense logic. Using methods from algebraic proof theory, we show that the logic has the finite model property. Combining with the finite axiomatizability of the logic, it follows that the logic is decidable.
Improved Visual Recognition Memory Model Based on Grid Cells for Face Recognition

Traditional facial recognition methods depend on a large number of training samples due to the massive turning of synaptic weights for low-level feature extractions. In prior work, a brain-inspired model of visual recognition memory suggested that grid cells encode translation saccadic eye movement vectors between salient stimulus features. With a small training set for each recognition type, the relative positions among the selected features for each image were represented using grid and feature label cells in Hebbian learning. However, this model is suitable only for the recognition of familiar faces, objects, and scenes. The model's performance for a given face with unfamiliar facial expressions was unsatisfactory. In this study, an improved computational model via grid cells for facial recognition was proposed. Here, the initial hypothesis about stimulus identity was obtained using the histograms of oriented gradients (HOG) algorithm. The HOG descriptors effectively captured the sample edge or gradient structure features. Thus, most test samples were successfully recognized within three saccades. Moreover, the probability of a false hypothesis and the average fixations for successful recognition were reduced. Compared with other neural network models, such as convolutional neural networks and deep belief networks, the proposed method shows the best performance with only one training sample for each face. Moreover, it is robust against image occlusion and size variance or scaling. Our results may give insight for efficient recognition with small training samples based on neural networks.
Conditional Neural Relational Inference for Interacting Systems

In this work, we want to learn to model the dynamics of similar yet distinct groups of interacting objects. These groups follow some common physical laws that exhibit specificities that are captured through some vectorial description. We develop a model that allows us to do conditional generation from any such group given its vectorial description. Unlike previous work on learning dynamical systems that can only do trajectory completion and require a part of the trajectory dynamics to be provided as input in generation time, we do generation using only the conditioning vector with no access to generation time's trajectories. We evaluate our model in the setting of modeling human gait and, in particular pathological human gait.
A Novel Spectral Zoom Method based on Zolotarev Polynomials

This paper discusses the usage of symmetrical Zolotarev polynomials (ZPS)s in spectral analysis. Evaluation of Discrete Zolotarev transform (DZT) coefficients is briefly discussed. However, the DZT coefficients evaluation is problematic. An alternative novel method embedding ZPSs is proposed. The novel method, so-called DZT zoom, improves the spectrum's time resolution for non-stationary signals compared to narrowband spectrogram. Results are comparable to the approximated DZT (ADZT). The DZT zoom distinguishing property is the focus on a particular frequency band of the spectrum.
Symbolic Deep Networks: A Psychologically Inspired Lightweight and Efficient Approach to Deep Learning

The last two decades have produced unprecedented successes in the fields of artificial intelligence and machine learning (ML), due almost entirely to advances in deep neural networks (DNNs). Deep hierarchical memory networks are not a novel concept in cognitive science and can be traced back more than a half century to Simon's early work on discrimination nets for simulating human expertise. The major difference between DNNs and the deep memory nets meant for explaining human cognition is that the latter are symbolic networks meant to model the dynamics of human memory and learning. Cognition-inspired symbolic deep networks (SDNs) address several known issues with DNNs, including (1) learning efficiency, where a much larger number of training examples are required for DNNs than would be expected for a human; (2) catastrophic interference, where what is learned by a DNN gets unlearned when a new problem is presented; and (3) explainability, where there is no way to explain what is learned by a DNN. This paper explores whether SDNs can achieve similar classification accuracy performance to DNNs across several popular ML datasets and discusses the strengths and weaknesses of each approach. Simulations reveal that (1) SDNs provide similar accuracy to DNNs in most cases, (2) SDNs are far more efficient than DNNs, (3) SDNs are as robust as DNNs to irrelevant/noisy attributes in the data, and (4) SDNs are far more robust to catastrophic interference than DNNs. We conclude that SDNs offer a promising path toward human-level accuracy and efficiency in category learning. More generally, ML frameworks could stand to benefit from cognitively inspired approaches, borrowing more features and functionality from models meant to simulate and explain human learning.
An Experiment to Design an Operation and Maintenance System Integrating Apriori Association Rules for a Telecom Platform

The operation and maintenance management of telecom platforms puts more emphasis on operation and alarm monitoring and less on business flow, and it is difficult to have a seamless connection between operation and maintenance and sales. To improve the cooperative efficiency and the informatization level of telecom platform operation and maintenance, a new operation and maintenance system integrating Apriori association rules for a telecom platform is proposed. In this paper, the Apriori algorithm is firstly used to analyse the fault correlation of the operation and maintenance system of the telecommunications platform to get the alarm message. Then, the fault risk of the operation and maintenance platform is evaluated intelligently by the system-business alarm causality model. Finally, based on the fixed end and the mobile end, the dynamic presentation of the system running state and alarm monitoring, device query and positioning, and online and offline inspection are implemented and designed, respectively. Experimental results show that the proposed operation and maintenance systems integrating Apriori association rules for the telecom platform innovate the cooperative mode between the operations and sales team, and the average time reduces to 3.9 minutes from 4.7 minutes and the risk forecasting accuracy increases from 8% to 26%. The assumed method can significantly improve the operational efficiency and intelligent level of the telecom platform’s system and business.
In Situ Visual Monitoring of Rotary Air Preheater Blockage: Setup and Image Analysis

Blockage of air preheaters has become a common problem in coal-fired power plants due to the formation of sticky ammonium bisulfate. The lack of efficient monitoring technique hinders the efforts to prevent the problem. In this paper, we propose a novel in-situ visual monitoring system on the cold end of the air preheater. The system is inexpensive but powerful enough to reveal the temporal evolution and spatial distribution of blockage fractions. By virtual of 3D printing, we built a lab-scale test structure of the contaminated corrugate plate of the air preheater, and took images under simulating dark environments. We revealed that integrating Gaussian filtration for noise elimination and then K-means clustering for image segmentation exhibits the best performance for images from nearly true circumstances. Moreover, the convolutional neural network manifests its ability to learn the blockage fraction and thus its future applicability for image processing with a well-labeled image dataset.
Shlomo Izre'el, Heliana Mello, Alessandro Panunzi and Tommaso Raso (eds.) (2020). In search of basic units of spoken language: a corpus-driven approach. (Studies in Corpus Linguistics 94.) Amsterdam &amp; Philadelphia: John Benjamins Publishing Company. Pp. xi + 440.

Shlomo Izre'el, Heliana Mello, Alessandro Panunzi and Tommaso Raso (eds.) (2020). In search of basic units of spoken language: a corpus-driven approach. (Studies in Corpus Linguistics 94.) Amsterdam &amp; Philadelphia: John Benjamins Publishing Company. Pp. xi + 440. - Volume 38 Issue 2
Label Distribution Learning by Mining Local Label Correlations in Self-regulating Clusters Independent of Sample Distance

Label distribution learning (LDL) is a framework to solve label ambiguity. To improve the performance of LDL, some existing algorithms exploit global and local label correlations. In reality, local correlations are more reasonable than global ones because few correlations can be globally adapted to all samples. The existing algorithms exploiting local correlations assume that the smaller the Euclidean distance between samples, the more likely the samples are to share the same label correlations. Specifically, they all use K-means to divide samples into several clusters, and then mine local correlations in each cluster based on this assumption. However, this assumption is incorrect in some cases and may lead to inappropriate clustering results and biased correlations. In this paper, we propose a novel LDL algorithm which mines local label correlations in self-regulating clusters independent of sample distance. In particular, we introduce clustering with learnable parameters into the model to realize that the clustering can be optimized jointly with the objective function instead of depending on the distance between samples. In this way, our proposed algorithm can mine more accurate local label correlations in these more appropriate clusters. Experimental results on 15 real-world datasets demonstrate the effectiveness of the proposed algorithm.
Method for Authentication of Diffie ‒ Hellman Values Based on Pre-Distributed Random Sequences and Wegman ‒ Carter One-Time Pad Algorithm

A method of authentication of keys generated by the Diffie-Hellman method is investigated in the context of the use of a man-in-the-middle attack by an attacker. It is assumed that the users Alice and Bob, who form the key, have pre-distributed random bit strings a and b, respectively, obtained either from some source or generated by themselves based on data obtained from magnetometers or accelerometers from mobile devices. The attacker has no access to these chains. A method for authenticating Diffie ‒ Hellman values (DH values) is proposed. For this purpose, the message (DH-value) is divided by Alice into N blocks. For each block, an authenticator is calculated using the Wegman ‒ Carter algorithm with a one-time pad keys, which are sequences a and b. The DH-value and authenticators are transmitted over the channel to Bob, who calculates authenticators from the received DH-value and compares them with the authenticators received from the channel. If the number of unauthenticated blocks does not exceed the set threshold, then DH-value authentication is considered successful. But the drawback of this method is a little disagreement between authenticating strings of different users. The formulas for probabilities of the undetected deception and the false alarm (due to the mismatch of chains a and b) are proved. The optimization of the method parameters (the number of blocks and the length of the authenticator) is carried out, at which the consumption of the authentication key (chains a and b) is minimized when the specified requirements for the probability of the undetected deception and the false alarm are met. Examples of the choice of authentication parameters for a 256-bit DH-value are given.