Reviewerly/exHarmony huggingface dataset:
Adaptive Resource Dimensioning with Joint Workload Placement for Cloud Stack Layers

Distributed cloud combines the best of telco and cloud technology and can run any application across multiple sites. Each site might be equipped with multiple virtualization layers (multi-layer cloud stack), e.g., OpenStack and/or Kubernetes. Workloads can be hosted in different virtualization layers. While this brings flexibility and elasticity for service provisioning, it also increases the complexity of life cycle management (LCM) during service instance design and assign. Especially, the compute and networking resources are typically overprovisioned due to a lack of an automated mechanism to scale them up/down or in/out according to their use. Adaptive resource dimensioning of cloud layers is needed to significantly smooth the process of service provisioning and to better utilize the virtualization layers’ resources. To address these challenges, in this paper, we propose a joint resource dimensioning and workload placement solution with multiple virtualization layers during service instance design and assign time. We demonstrate the feasibility of our solution with illustrative examples.
Performance Evaluation of HMI based on AHP and GRT for GUI

With consideration to the multi-factor and multi-level characteristics of the human-machine evaluation system for graphical user interface (GUI), the main factors affecting the interactive performance are analyzed. The evaluation system was established to evaluate the performance of human-machine interaction (HMI) for the GUI based on the analytic hierarchy process (AHP) and grey relational theory (GRT) model. Furthermore, we conducted an actual HMI experiment for four interactive products (desktop computer, intelligent refrigerator, smart car, and recreational machine) to verify the validation of the performance evaluation system. The application value of the evaluation model is demonstrated through the calculation. In conclusion, this study can provide a reference to designers for the scientific evaluation of human-machine performance of interactive products, which will help them design user-friendly interactive products with higher interaction efficiency.
Improvement of cold chain transportation client based on improved mediator pattern

The mediator pattern is often used in scenarios where multiple objects communicate. There are some defects and limitations in the use of traditional mediator pattern, and the code readability and maintainability are poor. Through the development of cold chain transportation client, this paper expounds the defects and limitations of the traditional mediator pattern in the communication between objects in Android system, and improves it based on the design principles and integrates the advantages of singleton pattern, observer pattern and appearance pattern to obtain the improved mediator pattern.
Psycho - social empowerment education based on choice theory in high - risk behaviors and hopeful of adolescent girls: Development and evaluation of efficacy

Psycho - social empowerment education based on choice theory in high - risk behaviors and hopeful of adolescent girls: Development and evaluation of efficacy
Cooperative Estimation of Local Atmospheric Delay in Precise Point Positioning with GNSS

In standard PPP GNSS, residual atmospheric delay (RAD) is estimated independently at each agent by filtering through time, resulting in estimation periods that are too long for vehicular applications. This paper explores cooperative estimation of local vertical RAD within a network of interacting agents. After formulating the problem, the paper formulates and compares: the individual solution when there is no inter-agent interactions, the centralized solution when all agents interact with a central node, and four distributed algorithms (i.e., information-weighted consensus (IWC), covariance intersection (CI), Inverse covariance intersection (ICI), and ellipsoidal intersection (EI)). The role of information shared between agents (i.e., cross-correlation) is specifically considered. The article shows that the distributed methods estimate the common RAD faster than independent agents. This increased accuracy enables faster and more accurate estimation of the unique portion of each agent's state (e.g., horizontal position).
Se<sup>2</sup>NAS: Self-Semi-Supervised architecture optimization for Semantic Segmentation

In this paper, we propose a Neural Architecture Search strategy based on self supervision and semi-supervised learning for the task of semantic segmentation. Our approach builds an optimized neural network (NN) model for this task by solving a jigsaw pretext problem identified by self-supervised learning over unlabeled training data, and, leveraging the structure of the unlabeled data with semi-supervised learning. Dynamic routing with a gradient descent approach is used to find the architecture of the NN model. Experiments on the Cityscapes and PASCAL VOC 2012 datasets show that the found neural network is four times more efficient than a state-of-the-art hand designed NN model in terms of floating-point operations.
A Comparative Look at the Resilience of Discriminative and Generative Classifiers to Missing Data in Longitudinal Datasets

Longitudinal datasets often suffer from the missing data problem caused by irregular sampling rates and drop-outs, which leads to sub-optimal classification performances. Given the breakthrough of deep generative models in data generation, this paper proposes a conditional Generative Adversarial Network (GAN) based longitudinal classifier (LoGAN) to address this problem in longitudinal datasets. LoGAN is evaluated against commonly used and state-of-art discriminative and generative classifiers. Comparative performance is presented showing the sensitivity of classifiers to data missingness, in both balanced and imbalanced datasets. Results show that the GAN-based models perform on par with other deep learning based models and perform comparatively better on a balanced dataset, while non-deep learning-based discriminative models, in particular the ensemble models, performed better when data was imbalanced. Specifically, F1 scores for LoGAN models were $$\ge $$ 80% for up to 20% of missing data rates in the temporal component of the dataset and $$\ge $$ 60% for missing rates from 40–100%. Non-deep generative models showed low performance with the introduction of missing data rates.
Classifying Imbalanced Data with AUM Loss

We present a significant improvement to a methodology which was described in several earlier articles by Barr, et al. where we demonstrated a workflow which classifies the source code of large open source projects for vulnerability. Whereas in the past, to deal with dearth of minority examples we’ve applied upsampling and simulation technique, this present approach demonstrates that a clever choice of cost function sans upsampling results in excellent performance surpassing previous results. In this iteration a feed-forward neural network classifier was trained on Area Under Min(FP, FN) (AUM) loss. The AUM method is described in Hillman & Hocking. Similar to earlier work, to overcome the out-of-vocabulary challenge, an intermediate step Byte-Pair Encoding which ‘compresses’ the data and subsequently, with the compressed data, long short-term memory (LSTM) network is used to embed the tokens from which we assemble an embedding of function labels. This results in 128D embedding which along with additional ‘interpretable’, heuristics-based features which are used to classify CVEs. The resulting labeled dataset is extremely sparse, with a minority class consisting of roughly 0.5% of total. Demonstratively, the AUM cost function is undeterred by sparsity of data; this is amply demonstrated by the performance of the classifier.
Solving HornSAT Fuzzy Logic Neuro-symbolic Integration

This research proposes a new method for improving logic programmingLogic programming efficiency in the Hopfield networkHopfield network. Basic logic program alone without integration with other computing networks have limits when modelling datasets due to its inability to capture all the data collection characteristics. An improved logical rule is required to retrieve the data sets’ hidden information. Hence, a new model of integrating fuzzy logic with HornSATHornSAT in the Hopfield network is proposed to represent information more effectively and in a comprehensive logical rule. The combination of fuzzy logicFuzzy logic and HornSAT can deal with the combinatorial optimization problems in Hopfield networks. Fuzzification and defuzzification processes can reduce the computing load to find the correct states of the neurons. The proposed approach can make the selection of neuron state wider between 0 and 1 in solving the HornSAT problem. Aside from that, during the defuzzification process, unsatisfied neuron clauses will be adjusted using the alpha-cutAlpha-cut method until the correct neuron state is identified. From the results shown, the proposed method proves to enhance the Hopfield network performance with effective results in terms of the global minimum ratioGlobal minimum ratio, hamming distanceHamming distance and processing periodProcessing period. The conclusion is fuzzy logic HornSAT increases the network performance better than the Direct technique.
Secure ECC based Key Exchange Mechanism for Devices in IoT Networks

Internet of Things (IoT) has now moved from research laboratories to user centric application development paradigm.This also raises the concern for security. Secure exchange of information between devices is a requirement of IoT, which is becoming challenging with the growth of IoT devices. Hence, to secure the transmission of information between the devices in IoT network, encryption algorithms are designed, but due to the resource limitation of the devices in IoT network,a lightweight design is required. In this paper, we present a elliptic curve based key exchange algorithm, which improves the security against active and passive attacks. Proposed mechanism exchanges keys securely between the devices in IoT networks. Proposed algorithm is implemented in Python scripting language and the performance is found to be good, the proposed scheme is also verified mathematically.
CAPE-NET: A Context-Aware Phrase-Enhanced Network for Classifying Mixed Multi-Turn Email Conversations and Explaining Predictions

SaaS CRM tools typically contain a rich repository of conversations between sales agents and prospective customers until the sales deal is won or lost. These conversations can occur over different channels such as email, chat, and voice calls. In such settings, conversation context and product purchase intent are spread across tens of message exchanges. To accurately predict the probability of a deal win, it is important to build classification models that capture such context continuity across a long chain of message exchanges. While past BERT-based work on modeling multi-turn conversations captures context history, they fall short when applied directly to sales negotiations which are not strictly multi-turn in nature, contain long messages, and are often interspersed with irrelevant “noise”. Additionally, extracting and communicating pivotal phrases in a customer message is a crucial undertaking as a part of model explainability. This requires additional attention to augmentation mechanisms keeping in mind the same context continuity. In this paper, we present a BERT-based novel architecture to achieve the above goals of conversation classification and prediction explainability. We train this architecture on a corpus of email exchanges available internally. Our architecture relies on a BERT-based multi-task training regimen trained for two tasks-one learns to predict deal closure from an email chain, and the other learns to extract multiple key-phrases from individual emails. The BERT setup also learns from short-term context to build email representations, which are further enhanced with extracted key-phrases using an attention-over-attention mechanism. Finally, Bi-LSTM layers capture long-term context across all phrase-enhanced emails in the conversation to predict deal closure. Experimental results demonstrate a~14-15% improvement in deal conversion accuracy and~10-12% improvement in Jaccard score for extracted phrases, outperforming our baseline model.
Studies on Winding Number in Abstract Cellular Complex

This paper the concept of the winding number and the computation of the winding number of the digital image in an abstract cellular complex is initiated. The notion of free homotopy on the abstract cellular complex between the loops is also introduced, and some of its properties are investigated by using the winding number of the loop with respect to a 0-dimensional cell.
Implementation of STEAM approach within the framework of educational engineering methodology

The article substantiates the importance of STEAM approach in the educational process implementation at all levels of education. The modern state of the Russian education system is briefly introduced and the most important tendencies of its present development and existing problems are highlighted. Authors explain the necessity of extending the range of methods and approaches to the Russian education system modernization. The article discusses the key ideas and thematic focuses of educational engineering methodology that provides the implementation of engineering design and support for educational process. The prerequisites to the development, key features and goals of STEAM approach are analyzed and the main ideas of its implementation are formulated. We study the correspondence of the main ideas of STEAM approach to the principles of the educational engineering concept. An elaborated methodology of STEAM approach implementation is described. Prospective directions of STEAM approach implementation in the current context are demonstrated.
Characteristic Analysis for Cooperatives Based on Cooperative Clustering in Cianjur

Cooperatives are a venture that can provide a solution to society's need for life-based on the spirit of help. The position of cooperatives in Indonesia is very important as one of the pillars of the economy. However, in the past few years, the number of cooperatives has steadily decreased because they have not fulfilled their duties and functions. This decline in the number of cooperatives also occurred in Cianjur, where around 70% of cooperatives died in 2018. Therefore, this study aims to analyze cooperatives in Cianjur based on clusters formed from the Cianjur cooperative database. The research data were obtained from the DKUPP Cianjur database. The data obtained totaled 1528 cooperatives with 8 attributes, namely the type of cooperative, cooperative group, business sector, number of members, own capital, external capital, business volume, and remaining business results. The cooperative cluster was formed using a combination of the Particle Swam Optimization and K-Means Clustering methods with the help of Rapid Miner. The result was three cooperative clusters in Cianjur with a Davies Bouldin index score of -1.54. The first cluster character was a thriving trading cooperative group or a developing trade cooperative group that had a fairly good business aspect, the character of the second cluster was an excellent financial service cooperative group or a cooperative with a very good financial condition, and the character of the third cluster is the unperforming financial cooperatives group or cooperative groups with poor financial conditions.
Self-adapting Trajectory Planning Method Considering Joint Constraints

When the robot interpolates the trajectory at high speed, the joint will exceed the constraint due to the constraints of driving mechanism performance and other factors. Based on the analysis of driver constraints, an adaptive trajectory planning method based on joint constraints is proposed. Firstly, maximum ranges of joint velocities in joint space are analyzed in advance by using the look-ahead strategy for speed control. Secondly, a maximum runnable resultant velocity in this path is calculated based on the linear relationship between joint velocity in joint space and path velocity in Cartesian space. Finally, the path velocity in Cartesian space is used for interpolation. The simulation and experimental results show that compared with the traditional method, the proposed Trajectory planning method can give full play to the driving performance of the robot joints while ensuring the real-time performance, taking into account the operating efficiency and constraint requirements of the robot.
Recommender Systems with Two-Stream Pyramid Encoder Network

Rate prediction has always been one of the most important tasks of Recommender Systems (RSs), and because of the popularity of Transformer in Deep Learning, we design a new deep learning model with Transformer to give a more accurate prediction. Following the idea of two-stream network, we propose a two-stream network architecture that incorporates Users' data and Item separately. Considering the new item may not have similar items in the dataset, we use feature pyramid networks (FPN) to extract a higher semantic segmentation of the item in Item-Stream. As for user data, we use Transformer block to extract the rating habit of a specific user. In this way, we reduce the prediction bias caused by different users' rating habits. In the decoder block, we jointly use the feature representations from the two-stream network to get the final rating prediction for a specific user to the new item. For different scale feature representation, we decode the feature separately and use the average value of predictions. Experimental results demonstrate that our model extracts higher level semantic information of the item. So, we overcome the problem of sparse data matrix.
A Kind of Cross-Camera Tracking Strategy in Tunnel Environment

In view of the fact that the traditional single camera tracking method cannot meet the real-time changing needs in the specific expressway tunnel environment, a Multi-Target Multi-Camera Tracking algorithm is proposed in this paper. Firstly, the target detection model based on PP-PicoDet is trained, and the accuracy of the risk object detection is improved by using the static object combined filtering method. Secondly, an improved DeepSORT algorithm is used to track the object vehicles in real-time. Then a Lane Based Time Mask algorithm is designed to constrain the track matching domain according to the characteristics of the tunnel. Finally, a Local Camera Group Clustering method based on hierarchical clustering is used for vehicle trajectory fusion under different cameras. The experimental results show that the algorithm realizes the real-time tracking and risk warning in the expressway tunnels with high precision.
Novel Abstraction and Offload Mechanisms for High Performance Cloud-native Distributed Object Stores

Object Storage solutions are typically optimised for capacity and cost but performance has traditionally been a second thought. We make the case for a highly performant distributed object store by a) building an abstraction layer to pass immutability and data affinity hints to the underlying storage while also, b) making the Objects layer aware of the hardware configurations enabling the Object Storage controllers to maximise throughput. With these optimizations we show that object storage performance can approach 95% of the maximum possible performance from the underlying raw storage while ensuring that the abstractions are generic enough to be able to run on any general purpose off the shelf storage systems. We have observed these performance gains across more than 1000 customer environments across diverse hardware. We also extend the above optimizations with generic mecha-nisms to offload compute closer to storage that have significant benefits for a broad class of workloads. Specifically, we evaluate performance gains from a) well known constructs like S3 Select for Analytics workloads and b) generic compute offload like Objects Lambda. This ability to offload compute is critical for modern distributed workloads like AI/ML and Analytics processing with very large distributed data sets.
An Improved Method for the UAV Coverage Path Planning

In UAV (Unmanned Aerial Vehicles) region detecting applications, the coverage path planning is very important for improving the searching efficiency due to the limitation of UAV’s flying endurance range. In this paper, we proposed an improved method for the UAV Coverage Path Planning, based on the tight-envelope algorithms to generate optimal coverage areas with no detecting region missed. Firstly, we used the Jarvis step algorithm to convexly envelop the target location. Then, an improved tight envelope algorithm was used to remove the invalid search area to form a concave search area. Finally, we used a concave-convexity adaptative algorithm to generate the UAV search path. In the simulation experiments with different threshold values, the feasibility and effectiveness of the algorithm were proved.
Weakly Supervised Whole Cardiac Segmentation via Attentional CNN

Whole-heart segmentation aims to delineate substructures of the heart, which plays an important role in the diagnosis and treatment of cardiovascular diseases. However, segmenting each substructure quickly and accurately is arduous due to traditional manual segmentation being extremely slow, the cost is high and the segmentation accuracy depends on experts’ level. Inspired by deep learning, we propose a weakly supervised CNN method to effectively segment the substructure from CT cardiac images. First, we utilize the deformable image registration technology to generate pseudo masks with high confidence for whole heart datasets, which can provide rich feature information to distinguish foreground and background. Meanwhile, the ground truth is used to cut patches containing more heart substructures so that the network can obtain more information about heart substructures. Then, we developed a novel loss function based on the weighted cross-entropy to enforce CNN to pay more attention to the tricky voxels nearby the boundary of cardiac substructures during the training stage. The proposed method was evaluated on MICCAI2017 whole heart CT datasets, and the overall segmentation score of 91.30%.
Up-Link/Down-Link Availability Calculation in Cloud Using Ford Fulkerson Algorithm

In recent days Cloud plays a vital role in Information technology. Many customers and organization are moving towards the cloud computing environment for effective utilization of the resources. Availability of Internet is the main need for accessing the cloud. Once the customer registered for a cloud service, they can access the service from anywhere and at any time using the Internet. Normally, shortest distance between the user interface and the information server in the Internet is identified using RIP (Routing Information Protocol) or OSPF (Open Shortest Path First Protocol). Ford Fulkerson algorithm is applied to shortest path graph to identify the uplink/downlink bandwidth. Hence this algorithm helps to identify the current availability percentage of cloud services. This algorithm is efficient when compared to the existing measurement methodologies like Trouble Ticketing, Device reach-ability and SNMP (Simple Network Maintenance Protocol).