Reviewerly/exHarmony huggingface dataset:
Low dimensional manifolds in reservoir computers

A reservoir computer is a complex dynamical system, often created by coupling nonlinear nodes in a network. The nodes are all driven by a common driving signal. Reservoir computers can contain hundreds to thousands of nodes, resulting in a high dimensional dynamical system, but the reservoir computer variables evolve on a lower dimensional manifold in this high dimensional space. This paper describes how this manifold dimension depends on the parameters of the reservoir computer, and how the manifold dimension is related to the performance of the reservoir computer at a signal estimation task. It is demonstrated that increasing the coupling between nodes while controlling the largest Lyapunov exponent of the reservoir computer can optimize the reservoir computer performance. It is also noted that the sparsity of the reservoir computer network does not have any influence on performance.
Utility of Missing Concepts in Query-biased Summarization

Query-biased Summarization (QBS) aims to produce a query-dependent summary of a retrieved document to reduce the human effort for inspecting the full-text content. Typical summarization approaches extract document snippets that overlap with the query and show them to searchers. Such QBS methods show relevant information in a document but do not inform searchers what is missing. Our study focuses on reducing user effort in finding relevant documents by exposing the information in the query that is missing in the retrieved results. We use a classical approach, DSPApprox, to find terms or phrases relevant to a query. Then, we identify which terms or phrases are missing in a document, present them in a search interface, and ask crowd workers to judge document relevance based on snippets and missing information. Experimental results show both benefits and limitations of our method compared with traditional ones that only show relevant snippets.
RGB-D Face Recognition via Spatial and Channel Attentions

After decades of extensive research, the field of 2D face recognition is fruitful. However, 2D face recognition is sensitive to changes in pose, facial expression and illumination. 3D face recognition has great potential due to the inherent invariance of pose and illumination changes, but these 3D face images are still difficult to acquire due to several issues such as cost and accessibility. In contrast, RGB-D images captured by low-cost depth sensors such as Kinect are relatively easy to acquire, and research on such low-quality inputs is still limited. In this thesis, an end-to-end multimodal fusion method based on spatial and channel attention is proposed to effectively fuse two image modalities, RGB and depth, to enhance RGB-D face recognition. Our work is specified as follows: firstly, three branches combined with the spatial and channel attention modules are established on the basis of ResNet18, so that the features of three modalities (RGB, depth map, and their fusion modalities, respectively) are obtained. The features under these three modalities are then fused, fed into a shared layer, and the fused features are further learned for deeper discriminative features and passed through a spatial attention vectorization module at the end. The method achieves good results on the Intellifusion RGBD dataset, while we perform ablation trials, which well reveal the contribution of each component of our method to the final performance.
Criteria for partial entanglement of three qubit states arising from distributive rules

It is known that the partial entanglement/separability violates distributive rules with respect to the operations of taking convex hull and intersection. In this note, we give criteria for three-qubit partially entangled states arising from distributive rules, together with the corresponding witnesses. The criteria will be given in terms of diagonal and anti-diagonal entries. They actually characterize those partial entanglement completely when all the entries are zero except for diagonal and anti-diagonal entries. Important states like Greenberger–Horne–Zeilinger diagonal states fall down in this class.
Least Conservative Linearized Constraint Formulation for Real-Time Motion Generation

Today robotics has shown many successful strategies to solve several navigation problems. However, moving into a dynamic environment is still a challenging task. This paper presents a novel method for motion generation in dynamic environments based on real-time nonlinear model predictive control (NMPC). At the core of our approach is a least conservative linearized constraint formulation built upon the real-time iteration (RTI) scheme with Gauss-Newton Hessian approximation. We demonstrate that the proposed constraint formulation is less conservative for planners based on Newton-type method than for those based on a fully converged NMPC method. Additionally, we show the performance of our approach in simulation, in a scenario where the Crazyflie nanoquadcopter avoids balls and reaches its desired goal in spite of the uncertainty about when the balls will be thrown. The numerical results validate our theoretical findings and illustrate the computational efficiency of the proposed scheme.
Design of Vocabulary Query System in Computer Aided English Translation Teaching

In Chinese universities, In translation facilities has not been well solved. This paper distinguishes machine translation from computer-aided translation, combines the process of computer translation, and points out the shortcomings of computer translation research. This paper proposes that the corresponding translation system should be combined universities, so as to cultivate market-oriented professional foreign language translation activities.
An Academic Text Recommendation Method Based on Graph Neural Network

Academic text recommendation, as a kind of text recommendation, has a wide range of application prospects. Predicting texts of interest to scholars in different fields based on anonymous sessions is a challenging problem. However, the existing session-based method only considers the sequential information, and pays more attention to capture the session purpose. The relationship between adjacent items in the session is not noticed. Specifically in the field of session-based text recommendation, the most important semantic relationship of text is not fully utilized. Based on the graph neural network and attention mechanism, this paper proposes a session-based text recommendation model (TXT-SR) incorporating the semantic relations, which is applied to the academic field. TXT-SR makes full use of the tightness of semantic connections between adjacent texts. We have conducted experiments on two real-life academic datasets from CiteULike. Experimental results show that TXT-SR has better effectiveness than existing session-based recommendation methods.
An Efficient Approach for Multiple User Data Security in Cloud Computing

Cloud computing can be a growing paradigm that includes growing to be today's best study location because of the power to slow up the charges connected with calculating. In today's time, it is best and also alluring engineering which can be supplying the particular companies to be able to the people about requirement online. In previous work, they proposed a method that can store only one user data. In the proposed method, a new algorithm can store multiple user's data and ensure the security levels. Initially, the user and cloud provider make a public key to encrypt the data. After that the data store in the cloud. If the user wants to get the data, the cloud provider authenticates the user by using public. In our proposed work to guarantee the protection associated with information, a new technique is suggested by applying the RSA formula. This formula is used to encrypt the information to supply protection make sure that merely simply the particular involved consumer can easily accessibility. Our proposed algorithm shows more than 75% of efficiency than the other algorithms. The user information is acquired to encrypt and saved in the cloud. When the required person/user places the request for the information of the cloud supplier, the cloud supplier authenticates the consumer, well as provides the information.
Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence

The Transformer-Kernel (TK) model has demonstrated strong reranking performance on the TREC Deep Learning benchmark---and can be considered to be an efficient (but slightly less effective) alternative to other Transformer-based architectures that employ (i) large-scale pretraining (high training cost), (ii) joint encoding of query and document (high inference cost), and (iii) larger number of Transformer layers (both high training and high inference costs). Since, a variant of the TK model---called TKL---has been developed that incorporates local self-attention to efficiently process longer input sequences in the context of document ranking. In this work, we propose a novel Conformer layer as an alternative approach to scale TK to longer input sequences. Furthermore, we incorporate query term independence and explicit term matching to extend the model to the full retrieval setting. We benchmark our models under the strictly blind evaluation setting of the TREC 2020 Deep Learning track and find that our proposed architecture changes lead to improved retrieval quality over TKL. Our best model also outperforms all non-neural runs ("trad") and two-thirds of the pretrained Transformer-based runs ("nnlm") on [email protected]
Turkish WordNet KeNet.

Currently, there are two available wordnets for Turkish: TR-wordnet of BalkaNet and KeNet. As the more comprehensive wordnet for Turkish, KeNet includes 76,757 synsets. KeNet has both intralingual semantic relations and is linked to PWN through interlingual relations. In this paper, we present the procedure adopted in creating KeNet, give details about our approach in annotating semantic relations such as hypernymy and discuss the language-specific problems encountered in these processes.
VTA-IH: A Fog-based Digital Forensics Framework

The wide acceptance of the Internet of Things (IoT) paradigm has contributed in recent years to a significant increase in the number of IoT applications and the existence of IoT devices generating large volumes of data. As a result, IoT devices have become vulnerable to cyberattacks. In addition, conventional digital forensics approaches are no longer effective for investigating a digital crime involving IoT devices and extracting traces across heterogeneous devices. To overcome these challenges, we propose VTA-IH, a fog-based digital forensics framework that employs a Complex Events Processing (CEP) model for intelligently identifying abnormalities in real-time events associated with IoT data streams. To this extent, we devise a Degree of Abnormality (DA) penalty mechanism that is adapted to identify vulnerabilities, threat, or attack patterns using multiple rules across devices and network-related events across fog environments. Throughout the paper, we discuss the architecture of the VTA-IH framework and demonstrate usefulness of the proposed CEP approach. Our proposed VTA-IH framework can be used in applications such as industrial IoT, autonomous vehicles, smart home systems, smart farming, among others.
Investigating Session Search Behavior with Knowledge Graphs

Knowledge graphs are widely used in information retrieval as they can enhance our semantic understanding of queries and documents. The main idea is to consider entities and entity relationships as side information. Although existing work has achieved improvements in retrieval effectiveness by incorporating information from knowledge graphs into retrieval models, few studies have leveraged knowledge graphs in understanding users' search behavior. We investigate user behavior during session search from the perspective of a knowledge graph. We conduct a query log-based analysis of users' query reformulation and document clicking behavior. Based on a large-scale commercial query log and a knowledge graph, we find new user behavior patterns in terms of query reformulation and document clicking. Our study deepens our understanding of user behavior in session search and provides implications to help improve retrieval models with knowledge graphs.
New perspective Reza gold deposit (Gedabek ore district, Lesser Caucasus, Azerbaijan)

&#x0D; &#x0D; &#x0D; The article describes Reza gold deposit of Ugur exploration area located in Geda- bek Ore District of the Lesser Caucasus in NW of Azerbaijan. It is established that main mineralization in the Reza gold deposit consists of hematite-barite-quartz-kaoline veins- veinlets and breccia, pyrite stock-stockverk and quartz-sulfide veins. On the main orebody surface center secondary quartzites with vein-veinlets barite-hematite mineralization have occured over which remain accumulations of hydrous ferric oxides cementing breccias of quartz and secondary quartzites. “Reddish mass” is also observed in erosion parts, being an oxidation product of stock and stockverk limonite-hematite ores. Representing typical gossans, these accumulations by the data of trenches for thickness about 5-10 m contain gold 0.3-3.5 ppm and silver 1.0-45.0 ppm. There are three zones of gold mineralization within the Reza gold deposit: oxide mineralization; transition zone mineralization; sulfide mineralization. The oxide gold mineraliza- tion consists of clay-gravel weathering crust of kaolinite type. The gold-bearing mineralization has been oxidized to a depth of ap- proximately 50-100 meters. Typically, the gold mineralization is coarser and a minor increase in gold grade occurs within the oxides compared to the original rocks. The nugget effect increase in the gold grade of the oxides does not exceed approximately 10%. Deposit alteration signature has characteristics which suggest the current outcrop level may be near the top of a mineralized, gold-bearing high sulfidation epithermal (HSE) system. The gold mineralization at the deposit is interpreted as forming in shallow high sulfidation epith- ermal systems. The mineralization has been noted in well-confined hydrothermal breccia and associated with pyrite stock-stockwork. The majority of the deposit material and current estimates are formed within the barite-hematite-quartz-kaoline mineralization in the secondary quartzite rocks. The main brecciation and stockwork are hosted within secondary quartzite, sometime massive silicified andesite porphyritic rocks.&#x0D; &#x0D; &#x0D;
Dynamic Selection of Weights of Ensemble Models Using Reinforcement Learning for Time-Series Forecasting

Ensemble models are powerful model building tools that are developed with a focus to improve the accuracy of model predictions. They find applications in time series forecasting in varied scenarios including but not limited to process industries, health care, and economics where a single model might not provide optimal performance. It is known that if models selected for data modelling are distinct (linear/non-linear, static/dynamic) and independent (minimally correlated models), the accuracy of the predictions is improved. Various approaches suggested in the literature to weigh the ensemble models use a static set of weights. Due to this limitation, approaches using a static set of weights for weighing ensemble models cannot capture the dynamic changes or local features of the data effectively. To address this issue, a Reinforcement Learning (RL) approach to dynamically assign and update weights of each of the models at different time instants depending on the nature of data and the individual model predictions is proposed in this work. The RL method implemented online, essentially learns to update the weights and reduce the errors as the time progresses. Simulation studies on time series data showed that the dynamic weighted approach using RL learns the weight better than existing approaches. The accuracy of the proposed method is compared with an existing approach of online Neural Network tuning quantitatively through normalized mean square error (NMSE) values.
Logistic Regression Based on Bat-Inspired Algorithm with Gaussian Initialization

Logistic regression is a supervised statistical learning method in the field of statistics and machine learning nowadays. The core idea of traditional logistic regression algorithm is to use gradient descent method to find the optimal parameter of loss function with certain convexity. As far as this kind of nonlinear problem is concerned, gradient descent (GD) method or stochastic gradient descent (SGD) method in linear search is generally used, however, there are some shortcomings of GD and SGD in dealing with the optimal parameter finding issue for loss function with certain convexity. Therefore, it is of great significance to study such issue that traditional logistic regression cannot properly handle. As a matter of fact, in the process of solving the optimal parameter of convex loss function, bat heuristic algorithm can be used to replace the traditional penalty function method, moreover, bat heuristic algorithm is a gradient independent, swarm intelligence algorithm, which can avoid falling into local minimum trap by dynamically controlling parameters. In this paper, batbased logistic regression algorithm has been proposed so as to deal with the multi-data classification problem, the proposed method can not only handle data without gradient information, but handle data without differentiable node transfer function as well, and its effectiveness has been strictly verified via numerical analysis.
Experimental Analysis of Dedicated GPU in Virtual Framework using vGPU

As the GPU technology is growing for quickening cloud computing work and graphic based calculations has prompted their incorporation in High Performance computing servers and Cloud frameworks. A few cloud service providers give virtual machine occurrences with GPU capacities.Using the appearance of virtualized GPU baremetal like NVIDIA's series vGPUs, assigning also, sharing actual GPU in virtual framework has become simpler and available at low cost. The utilization rate and degree of utilization are controlled by the virtual GPU booking calculation and a installed virtual GPU machine. As a component of this work, we present an intensive experimental analysis of the dedicated GPU in virtualized environment. Specifically, we evaluate the virtualization workload activities, study the obstruction impacts of simultaneously executing similar and multi tenant activity burdens, and effect of vGPU algorithm for scheduling. We additionally show that the best vGPU design specification are subtle to the multiple of work activity attributes that measure the GPU. Our research also analyze execution of vGPU based virtual machines with PCI rooted direct GPU tasks. Based on our result using multiple tasks setups and with diverse GPU configurations, we notify that the overheads for VMs are 7 to 9% with less memory availability and up to 20 to 22% with increasing run time. We also analyze co-placing of multiple workloads and increase the utilization and efficiency of vGPU with less execution time up to 20%.
Three Dimensional Wireframe Model of Medical and Complex Images Using Cellular Logic Array Processing Techniques

The main aim of Computer vision is to provide the representation of images which is helpful for a better understanding of Two Dimensional and Three-dimensional world. Considering this, in this paper we proposed a method called cellular logic array processing for Three Dimensional wireframe representation of 3D images. This approach detects the junctions, straight lines, curves, depth of the 3D image and constructs the 3D wireframe model for the given dataset. The proposed algorithm has been evaluated on example-based Synthesis of 3D Object Arrangements dataset, PASCAL 3D+ dataset, and our own medical images dataset. The time complexity of the proposed method is O(n3r3) + O(n3) and the Average processing time is 1.12 s. The number of passes required for converting an input image into a wireframe model is one that is less than the other methods. The proposed method is simpler and has better accuracy compared to other wireframe based methods.
An Investigation of Identity-Account Inconsistency in Single Sign-On

Single Sign-On (SSO) has been widely adopted for online authentication due to its favorable usability and security. However, it also introduces a single point of failure since all service providers fully trust the identity of a user created by the SSO identity provider. In this paper, we investigate the identity-account inconsistency threat, a new SSO vulnerability that can cause the compromise of online accounts. The vulnerability exists because current SSO systems highly rely on a user's email address to bind an account with a real identity, but ignore the fact that email addresses might be reused by other users. We reveal that under the SSO authentication, such inconsistency allows an adversary controlling a reused email address to take over associated online accounts without knowing any credentials like passwords. Specifically, we first conduct a measurement study on the account management policies for multiple cloud email providers, showing the feasibility of acquiring previously used email accounts. We further perform a systematic study on 100 popular websites using the Google business email service with our own domain address and demonstrate that most online accounts can be compromised by exploiting this inconsistency vulnerability. To shed light on email reuse in the wild, we analyze the commonly used naming conventions that lead to a wide existence of potential email address collisions, and conduct a case study on the account policies of U.S. universities. Finally, we propose several useful practices for end-users, service providers, and identity providers to protect against this identity-account inconsistency threat.
The code-mixing of the Senegalese migrants in Italy

Aims and objectives/purpose/research questions: This paper describes and analyses the speech from the Senegalese migrant community in the city of Pescara (Italy) from a contact-linguistics perspective. The focus is on the phenomena of code-mixing found when the Italian language, that of the host community, is added to the complex repertoire of Wolof/French multilingual speakers. Design/methodology/approach: The patterns of code-mixing and the composition of trilingual speech are presented and discussed following the Muysken grammatical model for classifying features of language-contact discourse. Data and analysis: Primary data have been collected recording spontaneous interactions among a group of first-generation migrants that have lived in Italy for about 10 years and are all speakers of a fluent learner-variety of Italian. Findings/conclusions: It will be shown that insertional and alternational code-mixing patterns are present in both the bilingual (French/Wolof) and the trilingual (Italian/French/Wolof) speech, but with different degrees of variation and distribution. Originality: Besides documenting the trilingual speech framed in a specific yet new migration contact setting, the author considers the role that the two languages of prestige, French and Italian, play within the structural composition of both types of code-mixing, especially with regards to their relationship with Wolof. Significance/implications: The results achieved contribute to the general theory of the grammatical model by reaffirming the properties of the patterns of insertion and alternation, but also by revealing other ambiguous forms of code-mixing that might suggest a future revision of the model.