Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing

Feature generation aims to generate new and meaningful features to create a discriminative representation space. A generated feature is meaningful when the generated feature is from a feature pair with inherent feature interaction. In the real world, experienced data scientists can identify potentially useful feature-feature interactions, and generate meaningful dimensions from an exponentially large search space in an optimal crossing form over an optimal generation path. But, machines have limited human-like abilities. We generalize such learning tasks as self-optimizing feature generation. Self-optimizing feature generation imposes several under-addressed challenges on existing systems: meaningful, robust, and efficient generation. To tackle these challenges, we propose a principled and generic representation-crossing framework to solve self-optimizing feature generation. To achieve hashing representation, we propose a three-step approach: feature discretization, feature hashing, and descriptive summarization. To achieve reinforcement crossing, we develop a hierarchical reinforcement feature crossing approach. We present extensive experimental results to demonstrate the effectiveness and efficiency of the proposed method. The code is available at https://github.com/yingwangyang/HRC_feature_cross.git

Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing

Feature generation aims to generate new and meaningful features to create a discriminative representation space.A generated feature is meaningful when the generated feature is from a feature pair with inherent feature interaction. In the real world, experienced data scientists can identify potentially useful feature-feature interactions, and generate meaningful dimensions from an exponentially large search space, in an optimal crossing form over an optimal generation path. But, machines have limited human-like abilities.We generalize such learning tasks as self-optimizing feature generation. Self-optimizing feature generation imposes several under-addressed challenges on existing systems: meaningful, robust, and efficient generation. To tackle these challenges, we propose a principled and generic representation-crossing framework to solve self-optimizing feature generation.To achieve hashing representation, we propose a three-step approach: feature discretization, feature hashing, and descriptive summarization. To achieve reinforcement crossing, we develop a hierarchical reinforcement feature crossing approach.We present extensive experimental results to demonstrate the effectiveness and efficiency of the proposed method. The code is available at https://github.com/yingwangyang/HRC_feature_cross.git

Reputation Revision Method for Selecting Cloud Services Based on Prior Knowledge and a Market Mechanism

The trust levels of cloud services should be evaluated to ensure their reliability. The effectiveness of these evaluations has major effects on user satisfaction, which is increasingly important. However, it is difficult to provide objective evaluations in open and dynamic environments because of the possibilities of malicious evaluations, individual preferences, and intentional praise. In this study, we propose a novel unfair rating filtering method for a reputation revision system. This method uses prior knowledge as the basis of similarity when calculating the average rating, which facilitates the recognition and filtering of unfair ratings. In addition, the overall performance is increased by a market mechanism that allows users and service providers to adjust their choice of services and service configuration in a timely manner. The experimental results showed that this method filtered unfair ratings in an effective manner, which greatly improved the precision of the reputation revision system

Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset

Event extraction (EE) is crucial to downstream tasks such as new aggregation and event knowledge graph construction. Most existing EE datasets manually define fixed event types and design specific schema for each of them, failing to cover diverse events emerging from the online text. Moreover, news titles, an important source of event mentions, have not gained enough attention in current EE research. In this paper, We present Title2Event, a large-scale sentence-level dataset benchmarking Open Event Extraction without restricting event types. Title2Event contains more than 42,000 news titles in 34 topics collected from Chinese web pages. To the best of our knowledge, it is currently the largest manually-annotated Chinese dataset for open event extraction. We further conduct experiments on Title2Event with different models and show that the characteristics of titles make it challenging for event extraction, addressing the significance of advanced study on this problem. The dataset and baseline codes are available at https://open-event-hub.github.io/title2event.Comment: EMNLP 202

A QoS-Satisfied Prediction Model for Cloud-Service Composition Based on a Hidden Markov Model

Various significant issues in cloud computing, such as service provision, service matching, and service assessment, have attracted researchers’ attention recently. Quality of service (QoS) plays an increasingly important role in the provision of cloud-based services, by aiming for the seamless and dynamic integration of cloud-service components. In this paper, we focus on QoS-satisfied predictions about the composition of cloud-service components and present a QoS-satisfied prediction model based on a hidden Markov model. In providing a cloud-based service for a user, if the user’s QoS cannot be satisfied by a single cloud-service component, component composition should be considered, where its QoS-satisfied capability needs to be proactively predicted to be able to guarantee the user’s QoS. We discuss the proposed model in detail and prove some aspects of the model. Simulation results show that our model can achieve high prediction accuracies

An Autonomic Optimization Model of Multi-Layered Dependability for Intelligent Internet of things

Accompanying with the speeding up of Internet of things (IOT) construction, the dependability problems become the important factors constraining its all-round development. Based on the multi-level and multidimensional properties of IOT dependability elements, with the overall improving of the dependability index of IOT as the ultimate goal, the dependability elements of the local fine-tuning in each layer, this paper researches the change rule of internal dependability elements in perception layer, network layer and business layer, and adopts perception layer as the example, using the method of linear programming to seek the best proportion of all kinds of dependability elements and the optimal values of the elements, trying to construct a feasible autonomic optimization model for dependability elements of IOT system. Firstly, according to the function features and dependability properties of each layer, and change rules between the dependability index and dependability elements in each layer are analyzed. Secondly, based on the dynamic changes (up or down) of dependability elements in internal environment (that is, three layers in IOT), the ratio relations of dependability elements in each layer are dynamically controlled and adjusted to implement the local optimization, improving the overall autonomic configuration and autonomic adjusting ability of IOT system. At last, example analysis results show that the optimization model proposed in this paper can realize the substantial optimization in each layer of IOT

Realizing high thermoelectric performance for p-type SiGe in medium temperature region via TaC compositing

SiGe is recognised as an excellent thermoelectric material with superior mechanical properties and thermal stability in regions with high temperatures. This study explores a novel strategy for co-regulating thermoelectric transport parameters to achieve high thermoelectric properties of p-type SiGe in the mid-temperature region by incorporating nano-TaC into SiGe combined ball milling with spark plasma sintering. By optimizing the amount of TaC in the SiGe matrix, the power factors were significantly increased due to the modulation doping effect based on the work function matching of SiGe with TaC. Simultaneously, the ensemble effect of the nanostructure leads to a significant decrease in thermal conductivity. Thus, a high ZT of 1.06 was accomplished at 873 K, which is 64 % higher than that of typical radioisotope thermoelectric generator. Our research offers a novel strategy for expanding and enhancing the thermoelectric properties of SiGe materials in the medium temperature range

Organic-2D composite material-based RRAM with high reliability for mimicking synaptic behavior

The field of artificial intelligence and neural computing has been rapidly expanding due to the implementation of resistive random-access memory (RRAM) based artificial synaptic. However, the low flexibility of conventional RRAM materials hinders their ability to mimic synaptic behavior accurately. To overcome such limitation, organic-2D composites with high mechanical properties are proposed as the active layer of RRAM. Moreover, we enhance the reliability of the device by ZrO2 insertion layer, resulting in stable synaptic performance. The Ag/PVA:h-BN/ZrO2/ITO devices show stable bipolar resistive switching behavior with an ON/OFF ratio of over 5 × 102, a ∼2 400 cycles endurance and a long retention time (>6 × 103s), which are essential for the development of high-performance RRAMs. We also study the possible synaptic mechanism and dynamic plasticity of the memory device, observing the transition from short-term potentiation (STP) to long-term potentiation (LTP) under the effect of continuous voltage pulses. Moreover, the device exhibits both long-term depression (LTD) and paired-pulse facilitation (PPF) properties, which have significant implications for the design of organic-2D composite material RRAMs that aim to mimic biological synapses, representing promising avenues for the development of advanced neuromorphic computing systems

Linear epitope identification of monoclonal antibodies against the duck Tembusu virus NS1

ABSTRACT: Since 2010, the duck Tembusu virus (DTMUV) has caused a severe outbreak of egg drop syndrome in laying ducks in China, which has resulted in substantial financial losses in the poultry industry. DTMUV nonstructural protein 1 (NS1), as the only secreted protein, could aid in the development of therapeutic antibodies and diagnostic techniques; however, there are few studies on the preparation and epitope identification of monoclonal antibodies (mAbs) against DTMUV NS1. In this study, by indirect enzyme-linked immunosorbent assay (ELISA), Western blotting, and indirect immunofluorescence assay, we screened 6 mAbs (8A4, 8E6, 10F12, 1H11, 3D5, 5C11) that could specifically recognize DTMUV NS1. For epitope mapping of mAbs, a series of GST-tagged truncated fusion proteins of DTMUV NS1 were constructed by prokaryotic expression. Finally, the 4 shortest linear epitopes were identified by indirect ELISA and Western blotting. The epitope 133FVIDGPK139 was recognized by 8A4, the epitope 243IPKTLGGP250 was recognized by 8E6, the epitope 267PWDEK271 was recognized by 10F12, and 156EDFGFGVL163 was recognized by 1H11, 3D5, and 5C11. By sequence alignment and cross-reaction tests, we found that 8A4 and 8E6 had high specificity for DTMUV NS1 compared with that of other mAbs, but 10F12, 1H11, 3D5, and 5C11 exhibited a clear degree of cross-reaction with dengue virus (DENV), Japanese encephalitis virus (JEV), West Nile virus (WNV), and Zika virus (ZIKV) NS1. Finally, the predicted crystal structure analysis showed the approximate spatial positions of the 4 epitopes on the NS1 dimer. In summary, our study revealed 2 specific mAbs for DTMUV NS1 recognition and 4 multiflavivirus mAbs for DENV, JEV, WNV, and ZIKV NS1 recognition