The quantity and variety of small electronic devices in the Internet of Things (IoT) are steadily increasing, leading to an urgent demand for wireless power supply methods for moving and multiple targets. This article proposes a novel tracking wireless power transfer (WPT) system for moving and multiple targets based on retrodirective array. The system includes a retrodirective power supply subsystem and power receiving targets. The principle of retrodirective system with different frequencies and its performance for multiple targets are analyzed and simulated. Real-time adaptive tracking of moving and multiple targets is achieved by beamforming according to beacon signals. Utilizing different frequencies and polarization enhances isolation between transmitting and receiving, enabling high-power and far-field WPT. A prototype of the proposed system is fabricated and measured. A single power-receiving target can receive a maximum of 24 dBm of 5.8 GHz microwave power at a distance of 5 m. After rectification, it can drive a temperature and humidity sensor and a communication module, allowing for environmental sensing and information feedback. The system can maintain continuous operation of the target while it moves within a wide 3D range of ±60∘. Additionally, continuous power supply with independent tracking for two targets is also achieved, verifying the tracking functionality for multiple targets.

Principal component analysis (PCA) is an important and widely used unsupervised learning method that determines population structure based on genetic variation. Genome sequencing of thousands of individuals usually generate tens of millions of SNPs, making it challenging for PCA analysis and interpretation. Here we present VCF2PCACluster, a simple, fast and memory-efficient tool for Kinship estimation, PCA and clustering analysis, and visualization based on VCF formatted SNPs. We implemented five Kinship estimation methods and three clustering methods for its users to choose from. Moreover, unlike other PCA tools, VCF2PCACluster possesses a clustering function based on PCA result, which enabling users to automatically and clearly know about population structure. We demonstrated the same accuracy but a higher performance of this tool in performing PCA analysis on tens of millions of SNPs compared to another popular PLINK2 software, especially in peak memory usage that is independent of the number of SNPs in VCF2PCACluster.

Double random-phase encoding- (DRPE-) based compressive sensing (CS) systems support image authentication for noisy images. When extending such systems to resource-constrained applications, how to ensure the authentication strength for noisy images becomes challenging. To tackle the issue, an efficient and secure image authentication scheme is presented. The phase information of the plain image is generated using DRPE and quantized into a binary image as the authentication information. Meanwhile, a sparser error matrix generated by the same plain image and vector quantization (VQ) image works as the input of CS. The authentication information and VQ indexes are self-hidden into the quantized measurements to construct the combined image. Then, it is permutated and diffused with the chaotic sequences generated from a modified Henon map. After decryption at the receiver side, the verifier can implement the blind authentication between the noisy decoded image and the reconstructed image. Supported by the detailed numerical simulations and theoretical analyses, the DRPE-CSVQ exhibits more powerful compression and authentication capability than its counterpart.

Since the turn of the century, hundreds of image watermarking algorithms based on histogram features have been reported. However, all the existing watermark embedding was based on binary embedding, and they only store two situations on two continuous bins, i.e., {0,1}. In this paper, we improve the existing embedding algorithms and propose a novel high-capacity watermarking algorithm by using two bins histogram modification. The new algorithm is based on the ternary numeral system, and watermark information is divided into three cases {0,1,2}. Specifically, we extract the histogram of the cover image first, then select the appropriate embedding range by two predefined thresholds, and last form the bin groups contained reasonable number of pixels. In the generation of watermark, we transfer the watermark information into a digital string W = w1w2... wt, wi∈ (0,1,2),i = 1,2,...,t. In the embedding algorithm, if wi= 2, we will adjust the height of two continuous bins, and let b'/a' ≥ T; if wi= 1, let a'/b' ≥ T; if wi= 0, let |a'-b'| ≤ 1, here a' and b' represent the height of the front and back bin, and T is the threshold. Experimental results show that the embedding capacity of the proposed watermarking algorithm is 60% higher than that of the existing algorithm. In addition, the new scheme can resist traditional geometric attacks.

Recent research shows that Collaborative Information Retrieval (CIR), in which two or more users collaborate on the same search task, has become increasingly popular. The presence of both search and collaboration behaviors makes CIR a complex search format, which further drives a critical need to understand CIR's search context. The contextual support for CIR should consider search contexts derived from both team members' search histories (including users' own search histories and partners' search histories) and their explicit collaboration (e.g., chatting). As it stands, existing studies on contextual search support only focus on Individual Information Retrieval (IIR) and only utilize individuals' own search histories. In this paper, we examine the unique search contexts (e.g., partners' search histories and team collaboration histories) in CIR. Based on a user study data collection with 54 participants, we find that compared to the use of individuals' own search histories, CIR contextual support is more effective when utilizing partners' search histories and teams' collaboration behaviors. More interestingly, though the explicit communication information (i.e., chat content) often involves massive noisy information, involving such noise does not affect the ranking of relevant documents since it also does not appear in relevant documents.

When using search engines, people often issue multiple related queries to accomplish a complex search task. A simple query-task structure may not fully capture the complexity of query relations since people may divide a task into multiple subtasks. As a result, this paper applies a three-level hierarchical structure with query, goal and mission - a mission includes several goals, and a goal consists of multiple queries. Particularly, we focus on analyzing query-goal-mission structure for mobile web search because of its increasing popularity and lack of investigation in the literature. This study has three main contributions: (1) we study the query-goal-mission structure for mobile web search, which was not studied before. (2) We identify several differences between mobile and desktop search patterns in terms of goal/mission length, duration and interleaving. (3) We demonstrate that the query-goal-mission structure can be applied to design better user satisfaction metrics. Specifically, goal-based search success rate and mission-based abandonment rate are better aligned with users' long-term engagement than query and session based metrics.

Mobile devices enable people to look for information at the moment when their information needs are triggered. While experiencing complex information needs that require multiple search sessions, users may utilize desktop computers to fulfill information needs started on mobile devices. Under the context of mobile-to-desktop web search, this article analyzes users’ behavioral patterns and compares them to the patterns in desktop-to-desktop web search. Then, we examine several approaches of using Mobile Touch Interactions (MTIs) to infer relevant content so that such content can be used for supporting subsequent search queries on desktop computers. The experimental data used in this article was collected through a user study involving 24 participants and six properly designed cross-device web search tasks. Our experimental results show that (1) users’ mobile-to-desktop search behaviors do significantly differ from desktop-to-desktop search behaviors in terms of information exploration, sense-making and repeated behaviors. (2) MTIs can be employed to predict the relevance of click-through documents, but applying document-level relevant content based on the predicted relevance does not improve search performance. (3) MTIs can also be used to identify the relevant text chunks at a fine-grained subdocument level. Such relevant information can achieve better search performance than the document-level relevant content. In addition, such subdocument relevant information can be combined with document-level relevance to further improve the search performance. However, the effectiveness of these methods relies on the sufficiency of click-through documents. (4) MTIs can also be obtained from the Search Engine Results Pages (SERPs). The subdocument feedbacks inferred from this set of MTIs even outperform the MTI-based subdocument feedback from the click-through documents.

The Experience Sampling Method (ESM) enables researchers to capture information about participants' experiences in the moment. Adding an end-of-day retrospective survey also allows participants to elaborate on those experiences. Although the use of photos in retrospective interviews and surveys for memory elicitation is well known, little research has investigated the use of photos in ESM studies. As smartphone adoption increases facilitating ESM studies and making photo sharing easier, researchers need to continuously evaluate the method and investigate the role of photos in such studies. We conducted a large-scale ESM and retrospective survey study via Android smartphones with more than 1,000 US participants, and analyzed participants' photo submissions, including how photo use correlated with participants' data quality and what, if any, value photos added for researchers. Our study sheds light on the role of photos in ESM and retrospective studies that researchers can reference when constructing future study designs.

Investigations of search processes that involve complex interactions, such as collaborative search processes, are important research topics. Previous approaches of directly applying individual search process models into collaborative settings have proven to be problematic. In this paper, we proposed an innovative approach to model collaborative search processes using Hidden Markov Model (HMM), which is an automatic technique for analyzing temporal sequential data. Obtained through a user study, the data used in this paper consist of two different tasks in both collaborative exploratory Web search and individual exploratory Web search conditions. Our results showed that the identified hidden patterns of search process through HMM are compatible with previous well-known models. In addition, HMM generates detailed information on the transitions of hidden patterns in search processes, which demonstrated to be useful for analyzing task differences, and for determining the correlation of search process with search performance. The findings can be used for evaluating collaborative search systems as well as providing guidance for the system design.

People search is an active research topic in recent years. Related works includes expert finding, collaborator recommendation, link prediction and social matching. However, the diverse objectives and exploratory nature of those tasks make it difficult to develop a flexible method for people search that works for every task. In this project, we developed PeopleExplorer, an interactive people search system to support exploratory search tasks when looking for people. In the system, users could specify their task objectives by selecting and adjusting key criteria. Three criteria were considered: the content relevance, the candidate authoritativeness and the social similarity between the user and the candidates. This project represents a first attempt to add transparency to exploratory people search, and to give users full control over the search process. The system was evaluated through an experiment with 24 participants undertaking four different tasks. The results show that with comparable time and effort, users of our system performed significantly better in their people search tasks than those using the baseline system. Users of our system also exhibited many unique behaviors in query reformulation and candidate selection. We found that users' general perceptions about three criteria varied during different tasks, which confirms our assumptions regarding modeling task difference and user variance in people search systems.

We propose a method to dynamically estimate the utility of documents in a search session by modeling the users' browsing behaviors and novelty. The method can be applied to evaluate query reformulations in a search session.

This paper presents a user study aiming to investigate the query reformulation in collaborative Web search. 7 pairs of participants were recruited and each pair worked as a team on two collaborative exploratory Web search tasks. Through the log analysis, we compared possible sources for participants to draw query terms from. The results show that both search and collaborative actions are possible resources for new query terms. Traditional resources for query expansion such as previous search histories and relevant documents are still important resources for new query terms. The content in chat and workspace generated by participants themselves seems more likely to be the resource for new query terms than that of their partners. Task types also affect the influences on query reformulations. For the academic task, previously saved relevance documents are the most important resources for new query terms while chat histories are the most important resources for the leisure task.

This paper presents an investigation on collaborative information seeking process of team users from the aspect of search tactics. Through the investigation, we identified team search tactics as essential components of collaborative search process. In the study, theory from both information science

Current search systems are designed to find relevant articles, especially topically relevant ones, but the notion of relevance largely depends on search tasks. We study the specific task that scientists are searching for worth-reading articles beneficial for their research. Our study finds: users' perception of relevance and preference of reading are only moderately correlated; current systems can effectively find readings that are highly relevant to the topic, but 36% of the worth-reading articles are only marginally relevant or even non-relevant. Our system can effectively find those worth-reading but marginally relevant or non-relevant articles by taking advantages of scientists' recommendations in social websites.

MOTIVATION: The next-generation high-throughput sequencing technologies, especially from Illumina, have been widely used in re-sequencing and de novo assembly studies. However, there is no existing software that can simulate Illumina reads with real error and quality distributions and coverage bias yet, which is very useful in relevant software development and study designing of sequencing projects. RESULTS: We provide a software package, pIRS (profile-based Illumina pair-end reads simulator), which simulates Illumina reads with empirical Base-Calling and GC%-depth profiles trained from real re-sequencing data. The error and quality distributions as well as coverage bias patterns of simulated reads using pIRS fit the properties of real sequencing data better than existing simulators. In addition, pIRS also comes with a tool to simulate the heterozygous diploid genomes. AVAILABILITY: pIRS is written in C++ and Perl, and is freely available at ftp://ftp.genomics.org.cn/pub/pIRS/.

Personalized search systems have evolved to utilize heterogeneous features including document hyperlinks, category labels in various taxonomies and social tags in addition to free-text of the documents. Consequently, classifiers, PageRank algorithms and Collaborative Filtering methods are often used as intermediate steps in such personalized retrieval systems. Thorough comparative evaluation of such complex systems has been difficult due to the lack of appropriate publicly available datasets that provide such diverse feature sets. To remedy the situation, we have created CiteData, a new dataset for benchmark evaluations of personalized search performance, that will be made publicly accessible. CiteData is a collection of academic articles extracted from CiteULike and CiteSeer repositories, with rich feature sets such as authors, author-affiliations, topic labels, social tags and citation information. We further supplement it with personalized queries and relevance judgments which were obtained from volunteer users. This paper starts with a discussion of the design criteria and characteristics of the CiteData dataset in comparison with current benchmark datasets, followed by a set of task-oriented empirical evaluations of popular algorithms in statistical classification, collaborative filtering and link analysis as intermediate steps for personalized search. Our results show significant performance improvement of personalized approaches, over that of unpersonalized approaches. We also observe that a meta personalized search engine that leverages information from multiple sources of features performs better than algorithms that use only one of the constituent source of features.