Reviewerly/exHarmony huggingface dataset:
Automating Lexical Complexity Measurement in Chinese with WeCLECA

Lexical complexity, generally understood as a multidimensional construct consisting of lexical density, sophistication, and diversity, has been recognized as an important construct in first language acquisition and second language acquisition. A large variety of lexical complexity measures have been proposed by researchers to study its relationship to second language learners’ writing and/or speaking proficiency. While this line of research has generated fruitful results for languages such as English and other Indo-European languages, less is known about how it may be applicable to a typologically distance language such as Chinese. In this paper, we report the design of a computational tool for automating the measurement of lexical complexity with 25 indices. The Batch Mode of the software supports analyses of a large number of .txt files and outputs results to a .CSV file suitable for importation into statistical packages for further analyses. In an example application, we analyzed 87 texts from three distinct registers (academic prose, fiction, and press reportage) from the Lancaster Corpus of Mandarin Chinese (McEnery and Xiao, The Lancaster Corpus of Mandarin Chinese: A corpus for monolingual and contrastive language study, in Proc. Fourth Int. Conf. Language Resources and Evaluation, eds. M. Lino, M. Xavier, F. Ferreira, R. Costa and R. Silva, (European Language Resources Association, Paris, 2014), pp. 1175–1178) and explored how register variation may manifest in lexical complexity. The ANOVA analyses showed linear increase in multiple lexical sophistication and diversity measures across academic prose, fiction and press reportage. The results showed that press reportage has lower lexical density, but higher lexical sophistication and diversity than academic prose; fiction also has higher lexical diversity than academic prose. This paper concluded with discussions of potential pedagogical applications for Chinese language teaching and learning.
An Adaptive Kalman Filter for Near Space Hypersonic Vehicle Tracking

Considering the characteristic of periodic ski-jump flight in the cruising stage of near space hypersonic vehicles, Sine-Jerk model is studied in this paper. Compared with other models, the Sine-Jerk model can further improve the matching degree of strongly maneuvering targets with periodicity. In practice, target maneuver angular rate is often unknown or even variable. When the actual maneuvering angular velocity deviates greatly from the preset value, the tracks filtering results will have deviation or even divergence. Therefore, in order to solve the above problems, this paper proposes a parameter adaptive Kalman filter algorithm, which can still have high tracking accuracy when the target angular rate changes. According to the filtering innovation, the algorithm judges whether the actual angular rate of the target matches the preset model and adaptively adjusts the filtering parameters by using the double-window detection method, so that the filtering gain matrix and the prediction error covariance can change adaptively with the change of the measurement residual. Compared with the Kalman filter algorithm with fixed tracking parameters, it is proved that the proposed algorithm has better filtering effect on the ski-jump maneuvering target in near space.
A Vision For Hierarchical Federated Learning in Dynamic Service Chaining

We have seen the tremendous expansion of machine learning (ML) technology in Artificial Intelligence (AI) applications, including computer vision, voice recognition, and many others. The availability of a vast amount of data has spurred the rise of ML technologies, especially Deep Learning (DL). Traditional ML systems consolidate all data into a central location, usually a data center, which may breach privacy and confidentiality rules. The Federated Learning (FL) concept has recently emerged as a promising solution for mitigating data privacy, legality, scalability, and unwanted bandwidth loss problems. This paper outlines a vision for leveraging FL for better traffic steering predictions. Specifically, we propose a hierarchical FL framework that will dynamically update service function chains in a network by predicting future user demand and network state using the FL method.
Bifrost: A Secure, Scalable and Efficient File Sharing System Based on Dual Deduplication

We consider the problem of sharing sensitive files across users while relying on a common, untrusted third-party, e.g., a Cloud Storage Provider (CSP). Although users can rely on a secure peer-to-peer (P2P) channel for file sharing, this introduces potential delay on the data transfer and requires the sender to remain connected while the transfer process occurs. Instead, users can upload information about the file on a common CSP and share only the essential information that enables the receiver to recover the original file. However, using encryption to protect the uploaded information from the untrusted cloud hinders deduplication capabilities, which is undesirable for CSPs. This paper introduces Bifrost, an innovative file sharing method inspired by recent results on dual deduplication. Bifrost achieves secure file sharing using an untrusted cloud, and simultaneously guarantees that (1) the CSP can efficiently deduplicate outsourced data; (2) the secure P2P channel is used only to transmit short, but crucial information; (3) users can check for data integrity, i.e., detect if the CSP alters the outsourced data; and (4) only the sender and the intended receiver can recover the sensitive file, i.e., the cloud or no malicious adversary can infer useful information about the shared file. We analyze compression and bandwidth performance using a proof-of-concept implementation. Our experiments show that secure file sharing can be achieved by sending as little as 640 bits on the P2P channel, irrespective of file size, while the CSP that aids the sharing can enjoy a deduplication rate of 86.9 %.
On the Implementation of a Software-Defined Memory Control Plane for Disaggregated Datacenters

By adopting a disaggregated hardware architecture, datacenters can achieve considerable efficiency gains and transition to a more sustainable and green future. By decoupling resources from a single monolithic server and connecting them through a high-speed optical network, it is possible to significantly increase resource utilization and reduce power consumption by consolidating workloads into fewer resource units. In this paper, we design and develop a software-defined control plane for disaggregated memory datacenters. Its core component, the Software-Defined Memory Controller, is the orchestrating software, which efficiently materializes the disaggregation concept. It accomplishes this by managing and monitoring remote resource pools, allocating resources to workloads, instrumenting the dynamic configuration of the underlying optical network for interconnecting remote compute and memory resources and interacting with software agents residing in host and guest Operating Systems for coordinating the attachment of remote memory. A major contribution of our design is the minimization of the delay for scheduling workloads and Virtual Machines in a disaggregated datacenter, which is accomplished with the efficient modelling of the disaggregated resources and networking elements into a graph for retrieving configuration data, as well as the optimization of the graph implementation. The architecture is based on the Everything-as-a-Service paradigm and is tightly coupled with OpenStack, the leading cloud infrastructure management software. Evaluation experiments validated the employment of a graph database system by the Software-Defined Memory Controller by demonstrating 58 percent faster query times than relational databases for a small-to-medium-sized datacenter, with the percentage increasing as the size of the datacenter grows.
NS3SaaS: Cloud-based “Network Simulator as a Service” with customisable resource scheduling

This paper presents the design, development and evaluation of a novel cloud-based Network Simulator as a Service. It wraps a discrete-event network simulator and deploys it as an elastic service, which can then be used for interactive and batch simulations execution, enhanced with results post-processing and analysis as well as presentation features. It addresses the requirement to rapidly carry out large numbers of network simulations in order to test and validate appropriate and domain specific optimisations in industry, research and education settings. This cloud based service drastically enhances the capabilities of a researcher by allowing arbitrary definition of simulation scenarios along with sets of parameters and commission of large scale simulation executions. It is based on an ensemble of efficient open-source tools, which, when properly coordinated, deliver to the end user a cloud based service that is novel, robust, extendible and extensible. It has been deployed within ESDA Lab (UoP, Greece) where several performance evaluation research scenarios were carried out testing different simulation scheduling schemes in order to evaluate the usefulness of our system and its efficiency under different prioritisation settings. Results demonstrate that, compared to the default general purpose network simulator, the offered cloud-based service significantly enhances end-users capabilities by tailor fitting to their different prioritisation requirements, e.g. favouring interactive users, minimising turnaround times, offering custom resources allocation, etc. In addition to that the offered service effectively hides the peculiarities of the standalone network simulator enabling its use by non-experts and significantly expanding the potential target group of end users.
Computing Experiment of LSB Detectors Set Reduction with Definite Reliability for 1-to-1 Pattern

The technology that allows to decreasing the set of steganalytical methods that determine the payload value in the image spatial domain using quantitative detectors of Least Significant Bits steganography. It consists of mathematical model, an algorithm for implementing the model and computer program. The mathematical apparatus of regression analysis is used. The results of the development and testing of the technique for reducing the set of steganalytical methods taking into account the accuracy and reliability to the diminution of the computational complexity of steganalytical expertise are presented.
An Improved Bayesian Loss Function for Crowd Counting

The annotations of all the crowd counting datasets so far are sparse binary matrices, so they cannot be used to supervise training directly. The mainstream crowd counting algorithm uses a Gaussian function to smooth each head label point, and then train their model by using it as “ground truth” density map. However, such “ground-truth” density maps are not perfect due to heavy occlusion, scale variation, background interference, etc. In this paper, we propose an improved BayesianLoss for the problems existing in the current crowd counting loss function. First of all, average distance to k-nearest neighbors is use to confirm the size of Gaussian likelihood estimation kernel for each labeled point to better distinguish the boundaries between crowds. Secondly, a new background likelihood estimation method is defined to better suppress the posterior probability of the edge background during training. In the evaluation of the mean absolute error metric, our method achieves state-of-the-art results on ShanghaiTech, UCF-CC-50and NWPU datasets. And on the largest dataset NWPU, our method outperforms the best loss-function-improving method DM-Count. At the same time, our loss function combined with other crowd counting models, such as MCNN, CAN, M-SFANet and TransCrowd, achieves better results than the original model.
An Improved Imbalanced Data Classification Algorithm Based on SVM

When we deal with most real-world classification problems, the collected datasets are mostly imbalanced. Dataset imbalance means that the number of samples of a certain class greatly exceeds the number of samples of other classes in the dataset, but often a minority class is the main object of our research. When classifying imbalanced datasets, it is easy to misclassify the minority class samples with higher misclassification costs. Therefore, the classification of imbalanced datasets is one of the main difficulties in the field of data mining. In this paper, we propose a support vector machine (SVM) algorithm based on improved whale optimization algorithm, called SWOA-SVM. This algorithm introduces the social group optimization algorithm (SGO) to optimize the problem that the WOA algorithm is prone to premature maturity, and improves the optimization process of the WOA. The performance of SWOA-SVM has been evaluated with SVM and other improved algorithms on multiple commonly used imbalanced datasets, using AUC, Accuracy and G-mean as performance evaluation criteria. The experimental results show that the algorithm can effectively improve the recognition rate of positive samples when dealing with different experimental datasets, which verifies the effectiveness of the algorithm.
Improving Fairness for QoE of Adaptive Video Streaming over ICN

Information centric network (ICN) improves the communication quality by flexible content-based control according to application-level information of video content. Then, adaptive video streaming is applied to ICN to improve the client’s quality of experience (QoE). In previous research for adaptive bitrate (ABR) algorithms for ICN, the buffer-based ABR algorithm (BBA), which is designed to change a bitrate according to the video buffer length of video player in a client, is effective for QoE of the single client. However, in terms of QoE fairness among BBA clients sharing a bottleneck, since each client adjusts a bitrate independently, the clients, which communicate with the lower bitrate video, have to decrease their bitrate even when the bottleneck link is occupied by the other clients communicating with the higher bitrate. This results in QoE unfairness and degradation of overall QoE for all clients. To improve overall QoE, this paper presents the approach to decrease bitrate differences among clients by increasing the bitrate of the lower bitrate clients before the bandwidth of bottleneck link is excessively occupied by communications for the higher bitrate clients in terms of QoE fairness. Then, we propose notification-based bitrate control in which distributed servers/routers detect congestion early and explicitly notify the lower bitrate clients to increase the bitrate before the bottleneck link is occupied, while encouraging the highest bitrate clients to decrease for congestion avoidance. Through the simulation experiments, the proposed system improves both QoE fairness and overall QoE metrics.
Homomorphic operations on integers via operations on bits

Homomorphic encryption allows us to process data in an encrypted form, and can be effectively used in secure cloud computing systems. To use homomorphic encryption in practice, it is often necessary to support arithmetic and logical operations within a single encryption algorithm. To achieve this goal, it is proposed to represent integers in binary form and implement homomorphic operations on integers through operations on homomorphically encrypted bits. Gentry's algorithm over bits is considered as an example, for which the implementation of addition, subtraction, and multiplication operations is proposed. The analysis of the received results is carried out.
Social Behaviour Identification relying on Device Fingerprint Analysis

Human beings are social animals and, therefore, their social interactions define themselves better than a username and a password. These social interactions can be monitored in an unattended way relying on the different kinds of devices carried by each individual thanks to wireless technologies and the Internet of Things devices. Each device has its own fingerprint which, combined with its connection context each time it consumes any cloud service or application, identifies itself in a unique way from any place and any time. This paper proposes a way to classify these social interactions for achieving to guess an identification for each individual based on their device fingerprint information gathered. The proposed solution is validated and evaluated using a use case with a real and completely anonymized dataset.
Remodeling Dynamical Systems with Variable Structure: Forming Initial Data Sets

This paper presents an algorithm to construct an input/output data set for remodeling dynamic systems with a variable structure. Remodeling is the process of constructing a mathematical model of a new class based on the existing mathematical models. The introduced approach is based on the formation of input/output data on the original model and their use for parametric identification of a new class models. Such problem formulation corresponds to the methods of the experi-ment design theory, but in a particular case (with deterministic initial models) does not provide repeated calculations with the same input data. There is also presented an example of the introduced approach for a mathematical model of the inertial torque transformer working process, which is a compound system of nonlinear differential equations.
Formalization of Data Exchange Between the Systems of Monitoring and Control over an Anaerobic Bioreactor

A conceptual scheme for monitoring and control of the technical state of a complex system - an anaerobic bioreactor - is proposed. A mathematical apparatus and the heart of its algorithmic software are presented as a basis for the synthesis of a smart system of control over the technological processes at production facilities. Examples of the analytical synthesis of an adaptive control system for a nonlinear multidimensional object and an observer algorithm for unmeasurable variables are presented.
Research on the Perception Method of Traffic Volume in Open Pit Mining Area Based on Yolov5 and Deepsort

The traffic volume of open-pit mining area is one of the indicators to measure the traffic saturation degree of open-pit mining area. Accurate perception of the traffic volume in the open-pit mining area can provide a basis for the dispatching and command of production scheduling and supervisal in the mining area, promote the improvement of transportation efficiency, enhance the transportation safety in the mining area, achieve intelligence for the construction of intelligent mine, and better meet the actual requirements of customers. On the scene of open-pit mining area, this paper uses the image acquisition module of RSU(roadside unit) at the height of the pit to detect traffic volume in open-pit mining area based on yolov5 and deepsort algorithm. The results of experiments show that this method can detect traffic volume accurately and effectively, and has strong robustness and good generalization ability.
On the New Inference Control Strategy in the Resolution Method

The paper presents the basic theoretical information of the formal theory of first-order predicates, proposes a new strategy based on the concept of a minimal clause, indicates its features and limitations on applicability. Illustrative examples allow us to demonstrate the features of using this strategy when checking the correctness of reasoning.
On the Deployment of Blockchain in Edge Computing Wireless Networks

Blockchain is the core technology at the heart of cryptocurrency such as Bitcoin and Ether. Due to its characteristics, e.g., immutability, decentrality, and consensus, it is now applied in several fields such as communications, e-health, and supply chains. Nevertheless, its integration to communication networks is facing several challenges related to power consumption, unreliable communication channels, and limited computing capacity of devices. In this paper, we focus on blockchain consensus optimization in edge computing wireless networks. Specifically, we propose a novel consensus mechanism adapted to the heterogeneity and dynamicity, e.g., mobility, of edge computing nodes. We call it adaptive blockchain for edge computing (ABEC). Through experiments, we demonstrate the superiority of ABEC over baseline consensus, in terms of block addition/confirmation latency and robustness to the edge nodes’ unreliable behaviour. Finally, the impact of several network parameters, e.g., data rate of master ruler/block’s owner, and number of transactions per block, is investigated.
A Fine Track Initiation Algorithm for Group Targets Based on Nearest Neighbor and Graph Theory

For the challenge of fine tracking initiation of dense targets within a group, a fine tracking initiation algorithm for group targets based on nearest neighbor and graph theory is proposed. First, a connectivity graph is established based on the distribution structure of the trajectory measurements and the nearest neighbor principle, then, a weighted adjacency matrix is established from the graph information, and finally, a depth-first traversal is performed based on the connectivity graph and the adjacency matrix to determine and optimize the fine starting trajectories of the targets within the group. The simulation verifies that the proposed algorithm improves the correct track initiation rate; reduces the false track initiation rate; significantly reduces the correlation of multiple point tracks between adjacent moments; and improves the overall accuracy of the initiated tracks compared with the modified logic method, DBSCAN-based and modified Hough transform multi-trail initiation algorithms.
Automation of the Tower Allocation Process of an Electric Power Transmission System

In this paper, a program capable of performing the automatic allocation of towers of an electrical energy transmission system, given a topographic profile, is presented. The code utilizes a GENETIC ALGORITHM (GA) as an optimization tool within the MATLAB software. Three genetic operators are used to obtain individuals: Reproduction, Crossover and Mutation. The fitness function evaluates the data related to the components and restrictions of the TRANSMISSION LINE (TL) to estimate the total cost that will serve as a selection parameter when applied in the GA.
Foreground Mask Estimation for Person Re-identification

Due to low resolution feature map and global average pooling, the features output from the standard baseline of person re-identification (ReID) are mixed with a large amount of background information during the construction of high-dimensional features. In order to solve the problem, we propose a new person ReID method based on foreground mask estimation. Firstly, in the data preparation stage, we make the key point area label for training set by using the human key point detection model OpenPose, which provides a reference for the design of loss function; Secondly, we propose foreground mask estimation for person ReID. HRNetv2-W32 is selected as the backbone to obtain a high-resolution feature map and a network branch is added after backbone to estimate the foreground mask which has a high distinction between foreground and background. We propose to map the mask onto the feature map to avoid introducing a large amount of background information. Moreover, a new loss function called Excess the Mean with Margin Loss (EMML) is proposed for mask-estimated branch, and mask visualization Experiments show multiple losses, including EMML, Triplet loss and ID loss, can ensure that foreground and background on the mask are clearly distinguished while they simultaneously supervise the training of model. In the experimental stage, we compare the feature maps obtained by our method with those obtained by baseline, which shows the effectiveness in eliminating background information. And we evaluate the proposed method on two public datasets, including Market1501 and DukeMTMC-ReID, Rank-1/mAP reached 95.0%/96.1% and 90.4%/79.8% respectively with only using global features.