Reviewerly/exHarmony huggingface dataset:
Few-shot short utterance speaker verification using meta-learning

Short utterance speaker verification (SV) in the actual application is the task of accepting or rejecting the identity claim of a speaker based on a few enrollment utterances. Traditional methods have used deep neural networks to extract speaker representations for verification. Recently, several meta-learning approaches have learned a deep distance metric to distinguish speakers within meta-tasks. Among them, a prototypical network learns a metric space that may be used to compute the distance to the prototype center of speakers, in order to classify speaker identity. We use emphasized channel attention, propagation and aggregation in TDNN (ECAPA-TDNN) to implement the necessary function for the prototypical network, which is a nonlinear mapping from the input space to the metric space for either few-shot SV task. In addition, optimizing only for speakers in given meta-tasks cannot be sufficient to learn distinctive speaker features. Thus, we used an episodic training strategy, in which the classes of the support and query sets correspond to the classes of the entire training set, further improving the model performance. The proposed model outperforms comparison models on the VoxCeleb1 dataset and has a wide range of practical applications.
A directionally selective collision-sensing visual neural network based on fractional-order differential operator

In this paper, we propose a directionally selective fractional-order lobular giant motion detector (LGMD) visual neural network. Unlike most collision-sensing network models based on LGMDs, our model can not only sense collision threats but also obtain the motion direction of the collision object. Firstly, this paper simulates the membrane potential response of neurons using the fractional-order differential operator to generate reliable collision response spikes. Then, a new correlation mechanism is proposed to obtain the motion direction of objects. Specifically, this paper performs correlation operation on the signals extracted from two pixels, utilizing the temporal delay of the signals to obtain their position relationship. In this way, the response characteristics of direction-selective neurons can be characterized. Finally, ON/OFF visual channels are introduced to encode increases and decreases in brightness, respectively, thereby modeling the bipolar response of special neurons. Extensive experimental results show that the proposed visual neural system conforms to the response characteristics of biological LGMD and direction-selective neurons, and that the performance of the system is stable and reliable.
Ensemble nonparametric variable selection method

Ensemble techniques are a method of generalizing multiple individual models to one optimal model, showing high predictive performance. It has the advantage of being available in both regression and classification problems, but it has the disadvantage that it is difficult to interpret the model due to its black-box characteristics. These shortcomings are supplemented by presenting a measure of variable importance, but this is only a relative value between explanatory variables, and there is a limit that the significance of each variable cannot be confirmed. On the other hand, in the case of the regression model, it is possible to test the significance of the variable through p-value and penalty. This study proposed a nonparametric variable selection method that tests the significance of explanatory variables based on the variable importance given in the ensemble technique. The simulation was conducted with eight models, and the results of sensitivity and specificity were compared by methodology. As a result of the simulation, the ensemble nonparametric variable selection showed better classification performance than the variable selection in the existing regression model in eight models. In addition, through case analysis, it was confirmed that the random forest nonparametric variable selection showed excellent performance even when there was a strong correlation between variables.
Challenges in metaverse in problem-based learning as a game-changing virtual-physical environment for personalized content development

The benefits and drawbacks of using the metaverse in the field of education are discussed, along with the possibilities and potential issues it will open up. Utilizing the idea of personalised knowledge transfer and the use of an immersive environment to match students&apos; learning pace using horizon classroom, the Metaverse environment can add a new dimension to the field of educational technologies. We have also highlighted the virtualization-based redesign of educational environments using a personalised cloud, which is anticipated to improve learning for students by making it more affordable, collaborative, and useful. The goal is to build a metaversity that can serve as a communication tool for teachers and students anywhere in the world by providing each with an individual avatar. This platform will offer a virtual setting for students to socialise with their friends and peers and receive peer-to-peer learning, which is lacking in the current e-learning environment. The students won&apos;t need an entrance score card; instead, they will need critical learning skills to advance and mature.
Planar Fourier optics for slab waveguides, surface plasmon polaritons, and 2D materials

Recent experimental work has demonstrated the potential of combining the merits of diffractive and on-chip photonic information processing devices in a single chip by making use of planar (or slab) waveguides. Here, arguments are developed to show that diffraction formulas familiar from 3D Fourier optics can be adapted to 2D under certain mild conditions on the operating speeds of the devices in question. In addition to serving those working in on-chip photonics, this Letter provides analytical tools for the study of surface plasmon polaritons, surface waves, and the optical, acoustic, and crystallographic properties of 2D materials.
Securing Signal Encryption Based on Reduced Round Homomorphic AES

Cryptography plays a vital role in protecting information that has increased as a result of digitalization.Personal sensitive information, including electrocardiogram (ECG) signals, is widely transferred around the world, and as a result, protecting the data from unauthorized access by attackers is critical.One of the algorithms that is frequently used is the advanced encryption standard (AES) algorithm, due to its remarkable reliability and its usage in a wide range of applications.However, key exchange is still necessary to execute computations on the encrypted data, whereas time is considered the essence of the efficiency of the encryption algorithm.This study proposes and investigates the use of the proposed improved reduced round AES algorithm in conjunction with the new proposed fully homomorphic encryption (FHE) in order to preserve data privacy and eliminate key exchange restrictions.This research was employed to process, encrypt, and decrypt digital ECG signals.The suggested algorithm improves the security level and encryption and decryption times by using fewer rounds of encryption.In order to achieve this goal, it was suggested that the evaluation process be added to the AES algorithm as an extra level of security.With the evaluation process, it is possible to execute a number of computation operations homomorphically on the encrypted data without decrypting it.Additionally, the number of rounds in the AES encryption was decreased from 10 to 5. Results indicate that the proposed algorithm would take 5.39475 × 10 32 years to break, making it more efficient than the traditional AES and others.Also, the proposed algorithm has a classification sensitivity of 95.83% while simultaneously displaying an accuracy of 100%.Additionally, it may be utilized for real-time internet of things (IoT) applications.
Design of a Hybrid GWO CNN Model for Identification of Synthetic Images via Transfer Learning Process

Visual representation of synthetic images is very accurate, due to which it is difficult to differentiate them for their natural counterparts.Existing models that perform this differentiation are either very complex, or cannot be scaled for multidomain image sets.Moreover, the accuracy of these models depends directly upon type of dataset & feature sets used for training & validation purposes.To overcome these limitations, this paper proposes design of a Hybrid GWO CNN Model for identification of Synthetic Images for big data Applications.The proposed model initially extracts multidomain feature sets from input images, that includes wavelet, cosine, fourier & convolutional features.These features are processed via a grey wolf optimization (GWO) Model, that assists in improving inter-class feature variance while minimizing intra-class variance levels.The GWO Model identifies training & validation sets, thereby assisting the classification model to accurately differentiate between different image types.To perform this task, a variance-based fitness function was modelled that covers both inter-class & inter-class variance levels.This classification is performed via use of a transfer learning-based CNN Model, that extends VGG-19 for high-efficiency operations.The proposed model was tested on a large number of datasets including Synthetic Fruit, Unsplash, ESPL Synthetic Image, and Okazaki Synthetic Texture Image (OSTI) databases.Based on these datasets, accuracy, precision, computational delay, recall & AUC (Area Under the Curve) metrics were evaluated & compared with existing models.It was observed that the proposed model showcased 9.5% better accuracy, 8.3% higher precision, 6.5% better recall, and 3.9% faster performance when compared with existing models.Due to such high performance under different datasets, the proposed model is useful for large-scale synthetic-image identification use cases.
Idiosyncratic Fuzzy Low-Energy Adaptive Clustering Hierarchy

Clustering is one of the important functionalities of networking, which has a dominant impact on the performance in terms of Throughput, packet delivery ratio and communication delays.The challenge in performing the clustering process is more challenging for heterogeneous networks.The divergence between the member nodes is highly variable for a network that contains IoT nodes.As the result of vast availability of different IoT hardware, organizing them under one roof is a critical task which is very vital too.This work is intended to develop a new hierarchical based clustering to improve the performance and the lifetime of the IoT / Wireless sensor network nodes.Three legacy functional blocks such as idiosyncratic fuzzy energy estimator, cumulative fuzzy energy hierarchy builder and energy based cluster head exchanger are integrated in this work and submitted here as "Idiosyncratic Fuzzy Low-Energy Adaptive Clustering Hierarchy (IFLEACH)".An industrial standard network simulator is used to evaluate the performance of IFLEACH in terms of throughput, packet delivery rate, latency, end-to-end delay and energy consumption by simulating the real-worlds IoT network environment replica.An achievement around 11 kbps throughput and 4 % security level are obtained by the proposed method in high density network.
Multimodal Activity Detection for Natural Interaction with Virtual Human

Natural face-to-face human-robot conversation is one of the most important features for virtual human in virtual reality and metaverse. However, the unintended wake-up of robot is often activated with only Voice Activity Detection (VAD). To address this issue, we propose a Multimodal Activity Detection (MAD) scheme, which considers not only voice but also gaze, lip-movement and talking content to decide whether the person is activating the robot. A dataset for large screen-based virtual human conversation is collected from various challenging cases. The experimental results show that the proposed MAD greatly outperforms VAD-only method.
Load balancing techniques in cloud platform: A systematic study

In the current scenario, researchers have made a new invention and added to the computing paradigm every next second. Cloud computing is one of the most demanding, practical, accessible and extended technologies based on ‘pay as per use model’ and works on virtualisation via internet. Data sharing and accessing have become easy by properly organising various resources, such as storage, servers, development tools, software, etc, in cloud. Handling these resources has faced many challenges, such as cost management, system performance, migration, load imbalance, reliability and privacy etc. Load imbalance is one of the most important factors which are solved by load balancing techniques. This paper introduced the detailed classification of load balancing methods and techniques that are taken as a solution to overcome such problems and also helps future researchers. Also given is a proposed model for load balancing and some comparative studies of the heuristics methods based on platforms and simulator tools.
Sistem Deteksi Surel SPAM Dengan DNSBL Dan Support Vector Machine Pada Penyedia Layanan Mail Marketing

Mail marketing is an effective communication medium for users and internet providers. Many companies use email as a mean of communication with customers to ensure customers are not left behind with the latest information, and at once provide personalized offers to specific customers. However, not all emails that are sent reach mail inbox as expected. There are several factors as the cause including content that does not comply with the writing rules and tends to have SPAM signatures, invalid e-mail addresses, the sender domains are registered in the blacklist and so forth. Mail marketing service providers such as MTarget and Mailchimp must ensure that emails sent by their customers have no potential to become spam, because it can affect all of their mail marketing services will be blacklisted, thus promotional goals will not be achieved. In that case, a system is needed to check the e-mail that will be sent by the customer, to ensure that the e-mail will not detected as a spam by email service applications such as Gmail. This research produces an email validator system that can prevent sending emails that have the potential to become SPAM, so as to reduce the risk of a mail marketing service provider being blacklisted which results in delays in promotion via email and a decrease in marketing turnover. The proposed method used in this research is the Domain Name System-Based Blackhole List (DNSBL) to check the IP and the sending domain and the Support Vector Machine (SVM) to check the content of the email to be sent. The system developed has been functioning as expected and has an accuracy rate of 97.54% in detecting SPAM emails.
Application of Three-Dimensional Media of The Shape of The Earth's Surface for The Learning of Visually Impaired

Blind students cannot use their senses properly; they can learn by relying on their senses of touch and hearing. For this reason, it is necessary to have teaching media that depend on the importance of communication or hearing to recognize material on the shape of the Earth's surface. This research aims to increase the learning interest of blind students in the fabric of the Earth's surface in social science subjects. This research method is pre-experimental, with blind students being treated with 3D media and observing their responses. The application of 3D media in the form of the Earth's surface in learning for blind children involved eight students. It was implemented in three junior high schools in East Java, Indonesia. These schools include Special Schools in Bojonegoro, Surabaya, and Sidoarjo. The results of the application of 3D media show that using 3D learning media in the form of the Earth's surface in the teaching and learning process can generate new desires and interests, generate motivation, and stimulate learning activities, even bringing psychological influences on blind students. 3D learning media can also help improve students' understanding, present data excitingly and reliably, facilitate data retention, and obtain information about the shape of the Earth's surface.
Tracking and Collision Avoidance of Autonomous Vehicle Based on Adaptive Preview Path

3. 天津所托瑞安汽车科技有限公司 天津 300450)
UAV path planning based on improved ant colony algorithm

The traditional ant colony algorithm is inefficient to search, easy to fall into algorithm stagnation and local optimization problems in UAV path planning. To ensure that the UAV can avoid obstacles and fly safely, the ant colony algorithm is improved and optimized. First, the target planning area of the UAV is modeled in three dimensions using raster method. Secondly, the update rules of pheromones are improved, and the weight factors of pheromones and heuristics are adjusted. An unmanned aerial path planning algorithm based on improved ant colony algorithm is proposed to plan a safe and optimal path for the unmanned aerial vehicle. Finally, the simulation results show that the improved algorithm has a better flight path than the traditional algorithm.
SAT-Inspired Higher-Order Eliminations

We generalize several propositional preprocessing techniques to higher-order logic, building on existing first-order generalizations. These techniques eliminate literals, clauses, or predicate symbols from the problem, with the aim of making it more amenable to automatic proof search. We also introduce a new technique, which we call quasipure literal elimination, that strictly subsumes pure literal elimination. The new techniques are implemented in the Zipperposition theorem prover. Our evaluation shows that they sometimes help prove problems originating from Isabelle formalizations and the TPTP library.
Mapping the structural and tectonic objects of the Upper-Kama potassium salt deposit in the magnetic field

Introduction. Petrophysical properties of rocks and strata reflect the long history of the Upper Kama potassium salt deposit (VKMKS) formation and development (about 300 million years) as well as its various endogenous and exogenous geological and geochemical processes. VKMKS geological structure as well as past and present processes at the field are effectively studied by geophysical methods. Experience has shown that, considering the gigantic area of the salt strata development and multi-layer potassium-magnesium salt deposits, high-precision and detailed geophysical studies performed on most of the VKMKS territory are most effective from a geological viewpoint, namely, gravity survey at a scale of 1 : 25,000 and aeromagnetic survey at a scale of 1 : 10,000 together with other geological and geophysical data. VKMKS structural-tectonic objects occurrence conditions and magnetic properties of their rocks and strata, especially magnetic susceptibility, form the petrophysical basis for the aeromagnetic survey applied to map the objects. The task of mapping structural-tectonic objects on the VKMKS territory in a magnetic field is a specific task of using aeromagnetic survey to map structural-tectonic objects in a weakly magnetic sedimentary cover of the entire Perm Cis-Urals. Methods of research includes studying and analyzing the research data of the Perm Cis-Urals sedimentary cover rock magnetic properties (magnetic susceptibility). The morphology of structural-tectonic objects magnetic field anomalies in the Perm Cis-Urals sedimentary cover and the morphology of VKMKS structural-tectonic objects magnetic field anomalies are studied and analyzed. Research results. The analysis of sedimentary cover rocks magnetic susceptibility and aeromagnetic survey data interpretation showed that the structural-tectonic objects of the cover create anomalies in the magnetic field. The anomalies exceed the intensity of three times the root-mean-square measurement error, and therefore are mapped with confidence. Conclusions. It is reasonable to use the data of areal high-precision aeromagnetic survey at a scale of 1 : 10,000 together with other geological and geophysical methods to improve the reliability of mapping such important structural and tectonic objects as tectonic faults of various ranks, from deep faults to fracture zones, at VKMKS.
Digitization Technologies and Contributions to Companies towards Accounting and Auditing Practices

The purpose of this study is to examine the development of digital technology, including blockchain technology, cloud computing and big data contributing to companies, as well as accounting and auditing practices. The study method uses a systematic literature review (SLR) with a bibliometric approach. The results of the study show that the consistent use of blockchain technology, cloud computing and big data has contributed to increasing organizational effectiveness and efficiency, but the problem of high costs, as well as data security and confidentiality are the most important issues in using these three digital technologies.
A Preliminary study on deep geodynamic process of Xar Moron tectonic belt in eastern Central Asia Orogenic Belt

板块汇聚边缘的陆壳厚度变化与构造和岩浆过程的动态相互作用有着错综复杂的联系,也是对深部地球动力学背景的直接响应。西拉木伦构造带是中亚造山带东部重要的汇聚板块边界,查明其浅部构造变形及深部动力学过程对于理解中亚造山带构造演化具有重要意义。本文通过野外地质工作查明晚二叠世-早三叠世西拉木伦构造带的上地壳发育一系列北东向、轴面向南东倾的宽缓褶皱以及向南东逆冲的断层,变形样式属于薄皮构造,显示出由北西向南东挤压的单向构造应力背景,平衡剖面恢复显示此时期构造变形造成地表~30%的缩短以及~4km的浅部地壳增厚。利用林西地区火成岩全岩La/Yb比值和锆石Eu/Eu<sup>*</sup>参数构建的年龄-地壳厚度曲线揭示,二叠纪早-中期地壳厚度从49km连续减薄到33km,反映此时期整体处于伸展环境。二叠纪晚期至三叠纪初期,地壳厚度增加了~15km,峰值厚度达~48km,这个迅速的地壳增厚过程可能是岩浆作用导致的地壳垂向增生和构造作用产生的造山带物质堆叠综合作用的结果。本文根据构造带同汇聚期岩浆岩面积和地壳厚度估算造山作用形成了~11%的新生陆壳。同时,两个时期的深部壳幔相互作用方式也有不同,二叠纪早期西拉木伦构造带火成岩锆石的<i>ε</i><sub>Hf</sub>(<i>t</i>)值相对较高(6.1~19.9;均值10.1),<i>δ</i><sup>18</sup>O值较分散(5.1‰~8.3‰),指示岩浆在形成过程中有幔源物质的加入,示踪了林西地区深部与软流圈上涌有关的伸展过程;而晚二叠世至三叠纪初期花岗岩锆石<i>ε</i><sub>Hf</sub>(<i>t</i>) 值相对下降(-1.1~17.2;均值9.3),<i>δ</i><sup>18</sup>O值仍高于幔源值(＞5.9‰),揭示同源地幔岩浆的持续重融改造过程。综合沉积环境、地壳厚度变化、岩浆岩同位素变化、地壳增生量及地表单向构造应力背景等特征,本文提出西拉木伦构造带可能经历了地幔俯冲;而早-中二叠世的软流圈上涌和晚二叠世-早三叠世的下地壳及岩石圈地幔密度增大,可能是发生地幔俯冲的深部地球动力学原因。
Innovation and Development—New Strategies for Informatization Based English Teaching in Higher Vocational Colleges in the Era of "Internet+"

The arrival of "Internet +" has brought new challenges to the teaching of professional English.Teachers should correctly grasp the necessity of English teaching, work out a set of scientific and reasonable teaching plan, attach importance to mobilizing students' learning enthusiasm and ensure their active participation.This paper analyzes the important role of English education informatization in higher vocational colleges, and points out the feasible ways of its construction, so as to make better use of its role in English education and greatly improve its comprehensive quality of English.
Anomaly Analysis of Blockchain-based Decentralized Applications of Transportation

Decentralized applications (DApps) refer to open-source applications that utilize blockchain technology. DApps of Transportation (DoT), as an important application of blockchain-based IntelligentTransportation Systems (ITS), is a new focus for current research.The survival analysis and activeness analysis of DoT are common life cycle analysis methods to give an overall understanding of the development of DoT, while the abnormal transactions analysis and token analysis help to discover possible problems in transactions of DoT.This paper conducts an anomaly analysis of DoT from four aspects: survival, activeness, abnormal transactions, and tokens.The survival, activeness, and abnormal transaction analysis are based on the graph, which provides analyzers with intuitive visualizations to discover problems.Specifically, we propose a framework in which a graph of DoT transaction network is constructed and an efficient algorithm is adopted to detect abnormal patterns in the graph.Additionally, the token analysis is done to find the problematic tokens of DoT and provide correction suggestions to developers.Our work presents exquisite visualizations as well as solid analysis for DoT market.The results show our methods can locate abnormal transactions and tokens that deviate from standards.