Reviewerly/exHarmony huggingface dataset:
Object-Centric Video Anomaly Detection with Covariance Features

In this paper, we propose four different methods for object-centric anomaly detection in surveillance videos based on autoregressive probability estimation. By means of the methods we propose, normal (typical) events in a scene are learned in a probabilistic framework by estimating the features of consecutive frames taken from the surveillance camera. To decide whether an observation sequence (i.e. a small video patch) contains an anomaly or not, its likelihood under the modeled typical observation distribution is thresholded. Due to its effectiveness in object detection and action recognition applications, covariance features are used in this study to compactly reduce the dimensionality of the shape and motion cues of spatiotemporal patches obtained from the video segments. By employing an object detection module to determine the important active regions in a scene with high detection rate, we propose new long-short term memory (LSTM), linear regression, and Gaussian mixture based methods to model the probability density of observation sequences. The most successful methods we propose achieves an average performance of 0.843 and 0.935 AUC scores respectively on two publicly available benchmark datasets.
Single Image Dehazing Based on Dark Channel Prior with Optimal Scaling Factors

Today, single image haze removal has attracted much attention in the community of image restoration. A popular type of approach is based on dark channel prior (DCP) because of its simplicity and effectiveness. One of the problems in the original DCP scheme is the fixed scaling factors for atmospheric light and transmittance. This paper attempts to replace them with optimal scaling factors obtained by the whale optimization algorithm (WOA). However, the WOA requires a reference to find the optimal scaling factors. To avoid using full reference in the optimization process, we adopt a half-reference that requires no ground-truth image. The proposed approach is justified by several examples and compared with the DCP scheme and a modified DCP scheme. The proposed approach shows its superiority over the comparison schemes in most of the examples, but has an overexposure or less dehazed problems in the rest of the examples. More research should be conducted on the proposed approach for better performance.
Implementation and Demonstration of Real-time Point Cloud Streaming System using HoloLens

Demands for high immersive communications using 3D content are increasing. In order to contribute to development of this immersive communications, this paper reports a prototype implementation of real-time point cloud streaming system and shows live-action demonstrations of live point cloud streaming using HoloLens. From the demonstration, we confirm that the prototype system could have a potential capability of realization of live streaming without any point cloud playback interruptions.
Multimode quantum squeezing generation via multiple four-wave mixing processes within a single atomic vapor cell

Multimode quantum squeezing plays an essential role in the fields of quantum metrology and quantum information. In this paper, we first construct a three- and four-mode energy-level cascaded four-wave mixing system in a single <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mi /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>85</mml:mn> </mml:mrow> </mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">R</mml:mi> <mml:mi mathvariant="normal">b</mml:mi> </mml:mrow> </mml:mrow> </mml:math> vapor, and then analyze the quantum properties of the produced states, including the covariance matrix and the intensity squeezing with 11 possible Hamiltonians. In addition, the dressing field is applied to modulate the nonlinear susceptibility and the multimode quantum states. Our scheme allows active modulation of the quantum states integrated within the generation step, without the need for any post-operation of the optics. The mode number of the states also can be extended using more pump fields and the dressing effect. Our study provides a promising candidate to generate multimode quantum states and multimode quantum squeezing within a quantum device involved in the construction of practical quantum networks.
Blockchain Enabled Artificial Intelligence for Cybersecurity Systems

Blockchain is a prominent technology having a wide range of applications in the domain of cybersecurity, Artificial Intelligence, Internet of Things and many more. Blockchains have the benefits of being immutable, data not being damaged by the host machine, and strong confidentiality. The drawbacks are that they need a lot more energy to store the same amount of data and are prone to 51 percent attacks. Blockchains have the potential to greatly improve cybersecurity. Blockchain is an unchangeable distributed ledger that allows data to be stored without the involvement of a third party. The implementation of blockchain technology in artificial intelligence for cybersecurity has piqued researchers' curiosity. The emphasis of this chapter is on blockchain's applicability in cybersecurity. This chapter envisages the role of blockchain and AI in Cybersecurity. Moreover, the open challenges for blockchain enabled AI solutions for Cybersecurity systems are also discussed in this chapter.
ECDSA White-Box Implementations: Attacks and Designs from CHES 2021 Challenge

Despite the growing demand for software implementations of ECDSA secure against attackers with full control of the execution environment, scientific literature on ECDSA white-box design is scarce. The CHES 2021 WhibOx contest was thus held to assess the state-of-the-art and encourage relevant practical research, inviting developers to submit ECDSA white-box implementations and attackers to break the corresponding submissions.In this work, attackers (team TheRealIdefix) and designers (team zerokey) join to describe several attack techniques and designs used during this contest. We explain the methods used by the team TheRealIdefix, which broke the most challenges, and we show the efficiency of each of these methods against all the submitted implementations. Moreover, we describe the designs of the two winning challenges submitted by the team zerokey; these designs represent the ECDSA signature algorithm by a sequence of systems of low-degree equations, which are obfuscated with affine encodings and extra random variables and equations.The WhibOx contest has shown that securing ECDSA in the white-box model is an open and challenging problem, as no implementation survived more than two days. In this context, our designs provide a starting methodology for further research, and our attacks highlight the weak points future work should address.
Does Blockchain Technology Facilitate the Tax System in the Era of Industry 4.0?

The use of blockchain technology in the tax system is quite new and has not been studied so far in the context of developing nations. The study explores how blockchain technology can be applied to the indirect tax system of a developing country, specifically for electronic invoices. A sample of seven employees who were from different fields such as the Federal Board of Revenue (FBR), Institute of Charted Accountants of Pakistan (ICAP), private institutions and the commerce department were interviewed. Results revealed that Blockchain technology can be used to distribute safe tax data, such as the Tax invoices serial numbers, which will make submitting the Tax invoices serial numbers more efficient and faster. In addition, the Tax invoices' serial numbers transactions can be tracked and analyzed. government meant must pay attention to the peculiarities of blockchain technology while undertaking a design linked to the implementation of blockchain technology in tax systems.
EtherFuzz: Mutation Fuzzing Smart Contracts for TOD Vulnerability Detection

With the development of Internet of Things technology, the use of Internet of Things is expanding, and its security risk will become an important factor restricting the development of Internet of Things technology. The application of blockchain technology in the security field of the Internet of Things can improve security problems, and the blockchain has immutable characteristics. Therefore, it is particularly important to ensure the security of blockchain smart contracts. However, the order of transaction in smart contracts is easy to be operated by miners, and there is a relative lack of tools to detect TOD (transaction-ordering dependent) vulnerabilities. The current smart contract vulnerability detection methods have the problems of low efficiency and low accuracy. Therefore, based on the study of TOD vulnerability principle, this paper creatively highlights a mutation fuzzy testing method EtherFuzz to specifically detect TOD vulnerability in smart contracts. Use the intelligent contract ABI (application binary interface) to generate test cases, test the byte code of the intelligent contract, use TOD to test oracle to detect TOD vulnerabilities, and then, mutate the tested data to generate new test cases. Finally, the behavior of smart contract operation is recorded, and the fuzzy test process is controlled until the vulnerability is detected. The experimental results show that when 987 token contracts are selected as Ethereum test objects, the false-positive rate, detection time overhead, and detection storage overhead of EtherFuzz are reduced by 74.4%, 30.1%, and 28.1%, respectively. Therefore, EtherFuzz has high speed, efficiency, and accuracy in detecting TOD vulnerabilities and has excellent application value.
Computing using pulse collisions in lattices of excitable microlasers

Coupled excitable microlasers have been shown theoretically to sustain saltatory propagation of solitonic-like excitations and hold good promise for fabrication of advanced and integrated photonic processing circuits. By studying a model for evanescently coupled excitable microlaser lattices with integrated saturable absorber, we investigate how pulse interaction can lead to non trivial responses. In particular, we show in a three-port system that depending on the system parameters and geometry, two counter-propagating pulses from two input ports can collide and exit or not in a third port, thus giving rise to the not-linearly separable XOR response function and to universal logic gates.
A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets

Health mention classification deals with the disease detection in a given text containing disease words. However, non-health and figurative use of disease words adds challenges to the task. Recently, adversarial training acting as a means of regularization has gained popularity in many NLP tasks. In this paper, we propose a novel approach to train language models for health mention classification of tweets that involves adversarial training. We generate adversarial examples by adding perturbation to the representations of transformer models for tweet examples at various levels using Gaussian noise. Further, we employ contrastive loss as an additional objective function. We evaluate the proposed method on the PHM2017 dataset extended version. Results show that our proposed approach improves the performance of classifier significantly over the baseline methods. Moreover, our analysis shows that adding noise at earlier layers improves models’ performance whereas adding noise at intermediate layers deteriorates models’ performance. Finally, adding noise towards the final layers performs better than the middle layers noise addition.
An Academic and Professional Profile of IT Faculty in the U.S.

Information technology (IT) as a university discipline continues grow and evolve. For that reason, IT educators and administrators are interested in understanding the academic and professional profile of IT faculty. A comprehensive survey of IT faculty was conducted in spring 2021, and this paper presents data that builds an academic and professional profile of both part time and full-time faculty in the U.S.
PID Regulator and Fuzzy-Digital Filter Combination in the Thermoelement Control Task

The thermoelement control system basis on the fuzzy logic has been developed in this article. This system improves the stability characteristics of devices using a PID controller in their structure. This effect is achieved by reducing the output signal, the value of which is calculated using a fuzzy digital filter. The main aspect of the fuzzy digital filter is the use of the area ratio method during defuzzifing. The limitation of this method is the use of triangular membership functions. The experimental results presented in the article demonstrate the effectiveness of the proposed solution. The study has shown that the time of transient processes with the combined control of the thermoelement is reduced by 16—22 %.
Automatically Drafting Ontologies from Competency Questions with FrODO

We present the Frame-based ontology Design Outlet (FrODO), a novel method and tool for drafting ontologies from competency questions automatically. Competency questions are expressed as natural language and are a common solution for representing requirements in a number of agile ontology engineering methodologies, such as the eXtreme Design (XD) or SAMOD. FrODO builds on top of FRED. In fact, it leverages the frame semantics for drawing domain-relevant boundaries around the RDF produced by FRED from a competency question, thus drafting domain ontologies. We carried out a user-based study for assessing FrODO in supporting engineers for ontology design tasks. The study shows that FrODO is effective in this and the resulting ontology drafts are qualitative.
From Open Set Recognition Towards Robust Multi-class Classification

The challenges and risks of deploying deep neural networks (DNNs) in the open-world are often overlooked and potentially result in severe outcomes. With our proposed informer approach, we leverage autoencoder-based outlier detectors with their sensitivity to epistemic uncertainty by ensembling multiple detectors each learning a different one-vs-rest setting. Our results clearly show informer’s superiority compared to DNN ensembles, kernel-based DNNs, and traditional multi-layer perceptrons (MLPs) in terms of robustness to outliers and dataset shift while maintaining a competitive classification performance. Finally, we show that informer can estimate the overall uncertainty within a prediction and, in contrast to any of the other baselines, break the uncertainty estimate down into aleatoric and epistemic uncertainty. This is an essential feature in many use cases, as the underlying reasons for the uncertainty are fundamentally different and can require different actions.
Problems of preparing future primary school teachers for the development of students ' mathematical abilities

The article is devoted to the issues of improving the professional training of future primary school teachers. A primary school teacher is a capable person who is engaged in his professional activities at a high level and knows how to design his professional development further. In this regard, the teacher should be in constant search. Based on the study of normative materials, philosophical, psychological and pedagogical research in the field of education, the relevance of the development of mathematical education and the preparation of future primary school teachers for the development of students ' mathematical abilities is determined. In the article kontseptualnye approaches underlying the training of future primary school teachers, the requirements to the content of primary education, aimed at developing the individual abilities of younger schoolchildren, and also requirements to qualifications and competencies of teachers. The author has determined the pedagogical conditions for preparing future primary school teachers for the development of students ' mathematical abilities, which include interdisciplinary interaction and integration of disciplines within the educational program.
Measuring 3D face deformations from RGB images of expression rehabilitation exercises

The accurate (quantitative) analysis of face deformations in 3D is a problem of increasing interest for the many applications it may have. In particular, defining a 3D model of the face that can deform to a 2D target image, while capturing local and asymmetric deformations is still a challenge in the existing literature. Computing a measure of such local deformations may represent a relevant index for monitoring rehabilitation exercises that are used in Parkinson’s and Alzheimer’s disease or in recovering from a stroke. In this study, we present a complete framework that allows the construction of a 3D Morphable Shape Model (3DMM) of the face and its fitting to a target RGB image. The model has the specific characteristic of being based on localized components of deformation; the fitting transformation is performed from 3D to 2D and is guided by the correspondence between landmarks detected in the target image and landmarks manually annotated on the average 3DMM. The fitting has also the peculiarity of being performed in two steps, disentangling face deformations that are due to the identity of the target subject from those induced by facial actions. In the experimental validation of the method, we used the MICC-3D dataset that includes 11 subjects each acquired in one neutral pose plus 18 facial actions that deform the face in localized and asymmetric ways. For each acquisition, we fit the 3DMM to an RGB frame with an apex facial action and to the neutral frame, and computed the extent of the deformation. Results indicated that the proposed approach can accurately capture the face deformation even for localized and asymmetric ones. The proposed framework proved the idea of measuring the deformations of a reconstructed 3D face model to monitor the facial actions performed in response to a set of target ones. Interestingly, these results were obtained just using RGB targets without the need for 3D scans captured with costly devices. This opens the way to the use of the proposed tool for remote medical monitoring of rehabilitation.
Analysis of the Effect of Classroom Reform of English Literature on the Theme of Environmental Protection in Universities Based on Artificial Intelligence Technology

Under the current theme of environmental protection, the effect of English literature classroom teaching reform cannot be analyzed qualitatively. Based on BP neural network, we present an analytical model of the impact of College English Literature Classroom Teaching Reform on environmental protection based on artificial intelligence technology. We analyze the teaching reform of English literature classrooms with the theme of environmental protection under artificial intelligence technology and explore the concept of “intelligent education” and the construction path of university English ecological teaching mode. Based on the in-depth excavation of teaching information, this paper analyzes the impact of college English literature classroom reform on the theme of environmental protection. Combined with BP neural network, a scientific analytic hierarchy process index system model is established, and the weight relationship of various reform influencing factors is given relatively objectively. The scores of the model based on the BP neural network under the six effect analysis indicators of teaching attitude, teaching tools, reform plan, reform teaching content, classroom organization, and reform rationality are 90.97, 86.3, 80.4, 95.7, 84.8, and 87.4, respectively. The results show that this model has a good ability for analysis and evaluation, which also provides some new ideas and entry points for the reform of the current English classroom teaching model. This study has a certain contribution to the breakthrough of college English teaching goals.
A fast encryption method of social network privacy data based on blockchain

In order to overcome the problems of long encryption time and poor security in traditional social network privacy data encryption methods, this paper proposes a fast encryption method of social network privacy data based on block chain. Firstly, the fuzzy C-means clustering algorithm is used to cluster the privacy data in social network, so as to improve the operability of data and shorten the encryption time. Based on the clustering results, combined with the blockchain structure, the elliptic curve encryption algorithm in the blockchain is used to realise the fast encryption of social network privacy data. The experimental results show that the encryption efficiency and encryption security of the proposed method are improved effectively, and the maximum encryption security coefficient reaches 0.98.
A statistically constrained internal method for single image super-resolution

Deep learning based methods for single-image super-resolution (SR) have drawn a lot of attention lately. In particular, various papers have shown that the learning stage can be performed on a single image, resulting in the so-called internal approaches. The SinGAN method is one of these contributions, where the distribution of image patches is learnt on the image at hand and propagated at finer scales. Now, there are situations where some statistical a priori can be assumed for the final image. In particular, many natural phenomena yield images having power law Fourier spectrum, such as clouds and other texture images. In this work, we show how such a priori information can be integrated into an internal super-resolution approach, by constraining the learned up-sampling procedure of SinGAN. We consider various types of constraints, related to the Fourier power spectrum, the color histograms and the consistency of the upsampling scheme. We demonstrate on various experiments that these constraints are indeed satisfied, but also that some perceptual quality measures can be improved by the proposed approach.
Movement of the Tongue During Target Reaching on a 2-Dimensional Surface

The tongue's physiological complexity and hidden location inside the oral cavity, limit our ability to quantify its fast and dynamic motions. Our team has developed a discreet wireless intraoral wearable device to study the tongue motion. In this paper, tongue movements were analyzed during functional tasks of target reaching when controlling a cursor on a computer screen. Ten healthy individuals participated in this study. Movement trajectories were compared to straight lines using the maximum perpendicular distance error. Results suggest that the tongue directs the cursor closer to a straight line when moving along the anteroposterior and mediolateral direction. We also observed that with practice the movement error along the mediolateral axis decreases more than the error for the anteroposterior and angular movements.