IOTA-Based Data Encryption Storage and Retrieval Method

At present,the traditional blockchain for data storage and retrieval reflects the characteristics of slow data uploading speed,high cost,and transparency,and there are a lot of corresponding problems,such as not supporting private data storage,large data operation costs,and not supporting Data field query.This paper proposes a method of data encryption storage and retrieval based on the IOTA distributed ledger,combined with the fast transaction processing speed and zero-value transactions of the IOTA blockchain,through the Masked Authenticated Messaging technology,so that the data is encrypted in the data stream.The form is stored in the distributed ledger,quickly retrieved through the field index mechanism established by the data form,and the data operation is carried out on the chain.Experimental results show that this system has high storage,encryption and retrieval performance,and good practicability.

Storage and retrieval of ultraslow optical solitons in coherent atomic system

We investigate the propagation of intense probe pulses in a lifetime broadened A-type three-level atomic system with a configuration of electromagnetically induced transparency. We find that ultraslow optical solitons formed by a balance between dispersion and nonlinearity can be stored and retrieved in the system by switching off and on a control field. Such pulses are robust during storage and retrieval, and hence may have potential applications in optical and quantum information processing.

Scheduling Storage Process of Shuttle-Based Storage and Retrieval Systems Based on Reinforcement Learning

The Shuttle-Based Storage and Retrieval System(SBS/RS)has been widely studied because it is currently the most efficient automated warehousing system.Most of the related existing studies are focused on the prediction and improvement of the efficiency of such a system at the design stage.Hence,the control of existing SBS/RSs has been rarely investigated.In existing SBS/RSs,some empirical rules,such as storing loads column by column,are used to control or schedule the storage process.The question is whether or not the control of the storage process in an existing system can be improved further by using a different approach.The storage process is controlled to minimize the makespan of storing a series of loads into racks.Empirical storage rules are easy to control,but they do not reach the minimum makespan.In this study,the performance of a control system that uses reinforcement learning to schedule the storage process of an SBS/RS with fixed configurations is evaluated.Specifically,a reinforcement learning algorithm called the actor-critic algorithm is used.This algorithm is made up of two neural networks and is effective in making decisions and updating itself.It can also reduce the makespan relative to the existing empirical rules used to improve system performance.Experiment results show that in an SBS/RS comprising six columns and six tiers and featuring a storage capacity of 72 loads,the actor-critic algorithm can reduce the makespan by 6.67%relative to the column-by-column storage rule.The proposed algorithm also reduces the makespan by more than 30%when the number of loads being stored is in the range of 7–45,which is equal to 9.7%–62.5%of the systems’storage capacity.

Adjusting amplitude of the stored optical solitons by inter-dot tunneling coupling in triple quantum dot molecules

We study the propagation properties of a probe field in an aligned asymmetric triple quantum dot molecule with both sides inter-dot tunneling coupling effect. It is shown that the probe field can form optical soliton due to the destructive quantum interference induced by the quantum inter-dot tunneling coupling effect. Interestingly, these optical solitons can be stored and retrieved by adjusting single or double inter-dot tunneling coupling effect, different from that light memory in the ultra-cold atom system. Furthermore, we also find that the amplitude of the stored optical soliton can be adjusted by the strength of the single or double inter-dot tunneling coupling. It is possible to improve the stability and the fidelity of the optical information in the process of the storage and retrieval in semiconductor quantum dots devices.

Query Performance Prediction for Information Retrieval Based on Covering Topic Score

We present a statistical method called Covering Topic Score （CTS） to predict query performance for information retrieval. Estimation is based on how well the topic of a user＇s query is covered by documents retrieved from a certain retrieval system. Our approach is conceptually simple and intuitive, and can be easily extended to incorporate features beyond bag- of-words such as phrases and proximity of terms. Experiments demonstrate that CTS significantly correlates with query performance in a variety of TREC test collections, and in particular CTS gains more prediction power benefiting from features of phrases and proximity of terms. We compare CTS with previous state-of-the-art methods for query performance prediction including clarity score and robustness score. Our experimental results show that CTS consistently performs better than, or at least as well as, these other methods. In addition to its high effectiveness, CTS is also shown to have very low computational complexity, meaning that it can be practical for real applications.

Learning to detect subway arrivals for passengers on a train

The use of traditional positioning technologies, such as GPS and wireless local positioning, rely on un- derlying infrastructure. However, in a subway environment, such positioning systems are not available for the position- ing tasks, such as the detection of the train arrivals for the passengers in the train. An alternative approach is to exploit the contextual information available in the mobile devices of subway riders to detect train arrivals. To this end, we pro- pose to exploit multiple contextual features extracted from the mobile devices of subway riders to precisely detecting train arrivals. Following this line, we first investigate poten- tial contextual features which may be effective to detect train arrivals according to the observations from 3D accelerome- ters and GSM radio. Furthermore, we propose to explore the maximum entropy （MaxEnt） model for training a train ar- rival detector by learning the correlation between contextual features and train arrivals. Finally, we perform extensive ex- periments on several real-world data sets collected from two major subway lines in the Beijing subway system. Experi- mental results validate both the effectiveness and efficiency of the proposed approach.

Location-Based Data Dissemination for Spatial Queries in Wireless Broadcast Environments

Most current research on Location-Based Services （LBSs, for short） assumes point-to-point wireless commu- nication, where the server processes a query and returns the query result to the user via a point-to-point wireless channel. However, LBSs via point-to-point wireless channel suffer from a tremendous amount of tramc and service requests from the user and thereby result in poor performance. In this paper, we present broadcast-based spatial query processing algorithms designed to support k-NN （k-Nearest Neighbor） and range queries via a wireless network. The task of the query processor is to selectively monitor the wireless broadcast channel, when the data items are disseminated by the server, according to their locations. Experiments are conducted to evaluate the performance of the proposed algorithms. Comprehensive experiments illustrate that the presented algorithms are highly scalable and are more efficient than the previous techniques in terms of both access time and energy consumption.