Reviewerly/exHarmony huggingface dataset:
Classification of Disaster Risks in the Philippines using Adaptive Boosting Algorithm with Decision Trees and Support Vector Machine as Based Estimators

This paper employed the intelligent approach based on machine learning categorized as base and ensemble methods in classifying the disaster risk in the Philippines. It focused on the Decision Trees, Support Vector Machine, Adaptive Boosting Algorithm with Decision Trees, and Support Vector Machine as base estimators. The research used the Exponential Regression for missing value imputation and converted the number of casualties, damaged houses, and properties into five (5) risk levels using Quantile Method. The 10-fold cross-validation was used to validate the proposed algorithms. The experiment shows that Decision Trees and Adaptive Decision Trees are the most suitable models for the disaster data with the score of more than 90%, more than 75%, more than 75% in all the classification metrics (accuracy, precision, recall f1-score) when applied to classification risk levels of casualties, damaged houses and damaged properties respectively.
KCB-BC-SSE: A Keyword Complete Binary Tree Searchable Symmetric Encryption Scheme using Blockchain

Internet of things (IoT) devices generate a massive amount of data every second in our lives. While cloud storage brings convenience to the users, it has the disadvantages of low scalability and weak reliability. Blockchain-based distributed storage eliminates data loss due to the tamper-resistant characteristic. Nonetheless, blockchain storage raises some confidentiality and retrieval efficiency issues of data. Searchable Symmetric Encryption (SSE) is an effective search scheme based on ciphertexts, which ensures data security. However, with the increase of data on the blockchain, the data retrieval on the blockchain will be difficult. In this paper, a Complete Binary Tree Searchable Symmetric Encryption using Blockchain (KCB-BC-SSE) scheme is proposed. According to the number of searchers, the scheme is divided into single searcher and multiple searchers where the search efficiency is improved in theory. And the simulation on ethereum shows the feasibility of our scheme. The security analysis illustrates our scheme achieves security under the chosen keyword attack.
Impact of fiber dispersion on the performance of entanglement-based dispersive optics quantum key distribution

Dispersive optics quantum key distribution (DO-QKD) based on energy-time entangled photon pairs is an important QKD scheme. In DO-QKD, the arrival time of photons is used in key generation and security analysis, which would be greatly affected by fiber dispersion. In this work, we established a theoretical model of the entanglement-based DO-QKD system, considering the protocol, physical processes (such as fiber transmission and single-photon detection), and the analysis of security tests. Based on this theoretical model, we investigate the influence of chromatic dispersion introduced by transmission fibers on the performance of DO-QKD. By analyzing the benefits and costs of dispersion compensation, the system performance under G.652 and G.655 optical fibers are shown, respectively. The results show that dispersion compensation is unnecessary for DO-QKD systems in campus networks and even metro networks. Whereas, it is still required in DO-QKD systems with longer fiber transmission distances.
An Optimal Separation of Randomized and Quantum Query Complexity

We prove that for every decision tree, the absolute values of the Fourier coefficients of a given order sum to at most , where is the number of variables, is the tree depth, and is an absolute constant. This bound is essentially tight and settles a conjecture due to Tal [Towards optimal separations between quantum and randomized query complexities, in Proceedings of the Sixty-First Annual IEEE Symposium on Foundations of Computer Science (FOCS), 2020, pp. 228–239]. The bounds prior to our work degraded rapidly with , becoming trivial already at . As an application, we obtain, for every integer , a partial Boolean function on bits that has bounded-error quantum query complexity at most and randomized query complexity . This separation of bounded-error quantum versus randomized query complexity is best possible, by the results of Aaronson and Ambainis [SIAM J. Comput., 47 (2018), pp. 982–1038] and Bravyi et al. [Classical Algorithms for Forrelation, arXiv preprint, 2021]. Prior to our work, the best known separation was polynomially weaker: versus for any [A. Tal, Towards optimal separations between quantum and randomized query complexities, in Proceedings of the Sixty-First Annual IEEE Symposium on Foundations of Computer Science (FOCS), 2020, pp. 228–239]. As another application, we obtain an essentially optimal separation of versus for bounded-error quantum versus randomized communication complexity for any . The best previous separation was polynomially weaker: versus (this is implicit in [A. Tal, Towards optimal separations between quantum and randomized query complexities, in Proceedings of the Sixty-First Annual IEEE Symposium on Foundations of Computer Science (FOCS), 2020, pp. 228–239]).
Efficiently learning halfspaces with Tsybakov noise

We study the problem of PAC learning homogeneous halfspaces with Tsybakov noise. In the Tsybakov noise model, the label of every example is independently flipped with an adversarially controlled probability that can be arbitrarily close to 1/2 for a fraction of the examples. We give the first polynomial-time algorithm for this fundamental learning problem. Our algorithm learns the true halfspace within any desired accuracy and succeeds under a broad family of well-behaved distributions including log-concave distributions.
Exploratory Data Analysis on Breast cancer dataset about Survivability and Recurrence

Exploratory Data Analysis (EDA) is an important step in data analysis where it helps Data Analysts and researchers represent the data visually and dig patterns from data to obtain deep knowledge ingrained in the dataset. In medical domain, data analysis primarily helps physicians and researchers in the field of health care where data about the patients is available in the form of text and images. To take the right choice in terms of cure and treatment, the analysis of the previous records of the patients helps most of the time. This proposed Exploratory Data Analysis analyzes the attributes: Nottingham Prognostic Index (NPI), the Overall Survival Status (OSS) and Relapse Free Status (RFS) from the Metabric Breast Cancer dataset to determine the survivability and disease recurrence among different age categories of breast cancer patients for 5-year and 10-years. The EDA is done using the visualization tools of Python and the observations from the data are represented using relevant swarm plots and tabulations. Comparison is also made in terms of NPI to the survival rates with that of the survival rates as reported from the datasets Breast Test Wales and Grimsby Breast Unit.
A Model for Relational to NoSQL database Migration: Snapshot-Live Stream Db Migration Model

Data migration is simply a process of transforming data from a source database to target database. Companies are moving their database from one database (e.g., RDBMS) to another new one (e.g., NoSQL) because of many reason such as new database can handle more data, that could be fast, scalable etc. There are various tools and methods such as, mongify [1], mongoimport [2], sqoop [3] etc. These are available for database migration from RDBMS to NoSQL databases. NoSQL uses document model, graph model, key-value etc., as compared to RDBMS databases. NoSQL was primarily designed for storing and retrieving a large amount of data. In this paper, we have proposed a database migration model, which can migrate real-time as well as old data in a parallel way. The proposed model is implemented for MySQL (RDBMS) to MongoDB document database in Java. The prototype migrates database instance at a particular time (Snapshot of database) as well as live data in a parallel way. Finally, our experimental evaluation of snapshot and live data migration shows the higher performance as compared to other methods.
Quality of Recommendations and Cold-Start Problem in Recommender Systems Based on Multi-clusters

This article presents a new approach to collaborative filtering recommender systems that focuses on the problem of an active user’s (a user to whom recommendations are generated) neighbourhood modelling. Precise identification of the neighbours has a direct impact on the quality of the generated recommendation lists. Clustering techniques are the solution that is often used for neighbourhood calculation, however, they negatively affect the quality (precision) of recommendations.In this article, a new version of the algorithm based on multi-clustering, \(M-CCF\), is proposed. Instead of one clustering scheme, it works on a set of multi-clusters, therefore it selects the most appropriate one that models the neighbourhood most precisely. This article presents the results of the experiments validating the advantage of multi-clustering approach, \(M-CCF\), over the traditional methods based on single-scheme clustering. The experiments focus on the overall recommendation performance including accuracy and coverage as well as a cold-start problem.
Data Visualization on the Life Cycle of Science and Technology Projects

Science and technology project management system is a fundamental tool in modern governments which is required for the effective management of research arrangement, financial resources, project implement and researcher communication. However, despite its successful development, there are still many visualized application scenarios that require customized methods to explore data integration, and to establish visualization of project life cycle. To that end, this paper focuses on science and technology project data model and business data model based on four phases of management workflow. Furthermore, we propose a project life cycle model, a three-layered data structure for combining all the data of a project, to facilitate dynamic integration among project data and business data. In addition, we present a system architecture including its functional layers and data flow, which improves project life cycle visualization, and especially displays the details of project and business data in a prototype web page. This research contributes to project management business visualization by providing data models and system architecture, and helps project managers in making decisions, managing business operations and tracking the progress of tasks.
Analytical Redundancy of Variable Cycle Engine Based on Proper Net considering Multiple Input Variables and the Whole Engine’s Degradation

In this paper, Proper net is proposed to construct variable cycle engine’s analytical redundancy, when all control variables and environmental variables change simultaneously, also accompanied with the whole engine’s degradation. In another word, Proper net is proposed to solve a multivariable, strongly nonlinear, dynamic, and time-varying problem. In order to make the topological structure of Proper net physically explainable, Proper net’s topological structure is designed according to physical relationship between variables, by which means analytical redundancy based on Proper net achieves higher accuracy with less calculation time. Experiments were compared with performance of analytical redundancy based on Proper net, seven convolutional neural network topological structures, and five shallow learning methods. Results demonstrate that under condition of average relative error less than 1.5%, Proper net is the most accurate and the least time-consuming one, which proves not only the effectiveness of Proper net but also the feasibility of topological structures’ design method based on physical relationship.
Research on Position and orientation of Wheel-wall Contact for Four-Wheel Wall-Climbing Robot Working on Cylindrical Tank

To study the adsorption performance of the split-type four-wheel wall-climbing robot working on the wall of the cylindrical tank, it is necessary to investigate the orientation of the robot and the position of the contact point between the wheel and the wall. This paper analyzes the contact situation between the wall and the wheels of a split-type four-wheel wall-climbing robot. The orientation of the left and right sides of the car body is then obtained. The orientation is described by a coordinate transformation matrix. Using the spatial geometric relationship and MATLAB, the orientation angle of the car body and the position of the four-wheel contact points relative to the angle of the left body attitude are obtained. The results provide a theoretical basis for designing the structure and size of the magnetic wheel.
Universally Composable Forward Secure Dynamic Searchable Symmetric Encryption

Kurosawa et al. formulate a universally composable (UC) security definition of dynamic searchable symmetric encryption (DSSE). They also show (weak) equivalence between the UC and the game-based definition, and introduce a UC secure DSSE scheme (KSOY scheme). However, there is a problem in their update protocol such that just documents containing the same keyword can be modified. Hence, the KSOY scheme does not satisfy forward security which guarantees that the server cannot know any relationship between an added or modified document and past search queries. In this paper, we introduce a new DSSE scheme such that documents containing different keywords can be modified, by extending the KSOY scheme. In addition, since Kurosawa et al.'s UC definition does not guarantee forward security, we extend their UC definition to guarantee forward security, and prove (weak) equivalence between the game-based definition of forward security and our UC definition. We also show that the proposed scheme satisfies UC security by proving the game-based definition.
Consistent Recovery Threshold of Hidden Nearest Neighbor Graphs

Motivated by applications such as discovering strong ties in social networks and assembling genome subsequences in biology, we study the problem of recovering a hidden 2k-nearest neighbor (NN) graph in an n-vertex complete graph, whose edge weights are independent and distributed according to Pn for edges in the hidden 2k-NN graph and Qn otherwise. The special case of Bernoulli distributions corresponds to a variant of the Watts-Strogatz small-world graph. We focus on two types of asymptotic recovery guarantees as n→ ∞: (1) exact recovery: all edges are classified correctly with probability tending to one; (2) almost exact recovery: the expected number of misclassified edges is o(nk). We show that the maximum likelihood estimator achieves (1) exact recovery for 2 ≤ k ≤ no(1) if liminf\frac 2αnlogn > 1; (2) almost exact recovery for 1 ≤ k ≤ o(\frac lognloglogn ) if liminf\frac kD(Pn||Qn)logn > 1, where αn \triangleq -2 log∫√{d Pn d Qn} is the Rényi divergence of order \frac 12 and D(Pn||Qn) is the Kullback-Leibler divergence. Under mild distributional assumptions, these conditions are shown to be information-theoretically necessary for any algorithm to succeed. A key challenge in the analysis is the enumeration of 2k-NN graphs that differ from the hidden one by a given number of edges. We also analyze several computationally efficient algorithms and provide sufficient conditions under which they achieve exact/almost exact recovery. In particular, we develop a polynomial-time algorithm that attains the threshold for exact recovery under the small-world model.
An Attention-based GRU Encoder Decoder for Hostload Prediction in a Data Center

Data center host load forecasting plays a key role in data center resource scheduling and energy saving. This paper proposes a recurrent neural network encoder and decoder host load prediction model based on the attention mechanism. Using the attention mechanism, the model can more accurately select the factors that affect the current host load. At the same time, through the analysis of the data, it is found that the memory occupancy rate of the host is similar to the host load to a certain extent, so the load of the host can be further accurately predicted by the feature of increasing the memory occupancy rate. The experiment uses a 29-day data center host load data set open sourced by Google. The experimental results show that the model proposed in this paper can achieve the best performance currently.
Access Control: Ciphertext Policy - Attribute Based Encryption in Cloud Computing

Abstract Access control and confidentiality is important features for cloud storage. The cloud service allows data owner to outsource their data to the cloud and through which provide the data access to the users. Although cloud computing brings many benefits, it may suffer from conventional distributed systems’ security attacks. Because the cloud server and the data owner are not in the same trust domain, the semi-trusted cloud server cannot be relied to enforce the access policy. However, storing the data in the untrusted cloud server leads the privacy and access control issues in the cloud. The traditional encryption schemes such as symmetric and asymmetric schemes are not suitable to provide the access control due to lack of flexibility and fine-grained access control. One of the prominent cryptographic technique to provide privacy and fine-grained access control in cloud computing is Attribute Based Encryption. In this paper, access control ABAC framework will be introduced for cloud storage systems that achieves finegrained access control based on an adapted Ciphertext-Policy Attribute-based Encryption (CP-ABE) approach.
A One-Step Approach to Covariate Shift Adaptation

A default assumption in many machine learning scenarios is that the training and test samples are drawn from the same probability distribution. However, such an assumption is often violated in the real world due to non-stationarity of the environment or bias in sample selection. In this work, we consider a prevalent setting called covariate shift, where the input distribution differs between the training and test stages while the conditional distribution of the output given the input remains unchanged. Most of the existing methods for covariate shift adaptation are two-step approaches, which first calculate the importance weights and then conduct importance-weighted empirical risk minimization. In this paper, we propose a novel one-step approach that jointly learns the predictive model and the associated weights in one optimization by minimizing an upper bound of the test risk. We theoretically analyze the proposed method and provide a generalization error bound. We also empirically demonstrate the effectiveness of the proposed method.
Learning auto-scale representations for person re-identification

Person re-identification (ReID) is a hot topic in computer vision. The data in the ReID is often collected from the cameras with different views and is affected by other environmental factors, which poses a significant challenge to ReID. The omni-scales proposed by OSNet can extract discriminative feature representations, which shows that omni-scales are adequate for the task of the ReID. However, the OSNet is mainly based on a manually designed network architecture. In the OSnet, each block uses the same architecture and has only four scale feature representations. Inspired by neural network architecture search (NAS), we propose a method of auto-scale representations for ReID. Specifically, we first design the auto-scale block, mainly composed of the Lite 3 × 3 operations and the RCB 1 × 1 operations. The connection status among the Lite 3 × 3 operations is just our search space. Then we give our entire macro network architecture, the auto-scale network, which is mainly composed of 6 auto-scale blocks. Unlike other NAS-related work, each block in our search space does not need to share the same architecture but can maintain a different architecture. In the search process, we propose the entropy regularization, the validity regularization and the consistent regularization to alleviate the discretized gap, no valid path, and meaningless edges, respectively. Finally, we verify the effectiveness of the model we searched on four commonly used datasets. Our model can maintain the same 2.2 M parameters as OSNet but can achieve the performance of SOTA. The mAP on the Market1501 dataset can reach 88.7%.
Automated Model of Comprehension V2.0

Reading comprehension is key to knowledge acquisition and to reinforcing memory for previous information. While reading, a mental representation is constructed in the reader's mind. The mental model comprises the words in the text, the relations between the words, and inferences linking to concepts in prior knowledge. The automated model of comprehension (AMoC) simulates the construction of readers' mental representations of text by building syntactic and semantic relations between words, coupled with inferences of related concepts that rely on various automated semantic models. This paper introduces the second version of AMoC that builds upon the initial model with a revised processing pipeline in Python leveraging state-of-the-art NLP models, additional heuristics for improved representations, as well as a new radiant graph visualization of the comprehension model.
Specific Time Embedding for Temporal Knowledge Graph Completion

The knowledge graph can be used as a corpus of cognitive computing, in this paper we mainly focus on the temporal knowledge graph. Temporal knowledge graph(TKG), as an extension of static knowledge graph(KG), can be used to deal with dynamic and time-varying knowledge in the real scenario, because many relations are only valid for a certain period, so it can ensure time consistency. Therefore, TKG has received more and more attention. KG embedding (KGE) is an enabling technique for KG completion(KGC), it can complete missing entities in tuples by discovering latent relations between representations. The previous methods mainly focus on static KGC(SKGC), with the emergence of TKG, temporal KGC(TKGC) should be developed. Currently, existing methods for TKGC, either consider changing the representation by temporal information or directly using temporal information to complete. In this paper, we inspired by quantum theory in a sense to propose specific time transE. We note that entities and relations are not time-restricted, only when they are combined to form tuples, the validity of tuples relies on time. We assume that entities and relations can get a determined status after being observed by a specific time, i.e., we use temporal information to get the specific representation of entities and relations. Tuples composed of these specific representations must be related to a specific time, and we use distance model transE to quantify correlation. Finally, through extensive experiments on TKGC datasets, the experimental results verify the validity of our models.
Spatial Reasoning and Context-Aware Attention Network for Skeleton-Based Action Recognition

Skeleton-based action recognition has achieved promising performance recently, but there are still many challenges, e.g., the structural relation between joints and the different attention of frames, due to the complex spatial-temporal evolution of skeletal joints. In this paper, we propose a spatial reasoning and context-aware attention network for skeleton-based action recognition, which consists of a spatial reasoning module and a context-aware attention module. The spatial reasoning module can exploit the structural relation between joints to obtain the spatial features within each skeleton frame, followed by the context-aware attention module learning the different attention of frames. We perform experiments on two datasets and verify the effectiveness of each module in the proposed network. The comparison results demonstrate that our method achieves state-of-the-art performance.