Most existing truth discovery approaches are designed for structured data, and cannot meet the strong need to extract trustworthy information from raw text data for its unique characteristics such as multifactorial property of text answers (i.e., an answer may contain multiple key factors) and the diversity of word usages (i.e., different words may have the same semantic meaning). As for text answers, there are no absolute correctness or errors, most answers may be partially correct, which is quite different from the situation of traditional truth discovery. To solve these challenges, we propose an optimization-based text truth discovery model which jointly groups keywords extracted from the answers of the specific question into a set of multiple factors. Then, we select the subset of multiple factors as identified truth set for each question by parallel ant colony synchronization optimization algorithm. After that, the answers to each question can be ranked based on the similarities between factors answer provided and identified truth factors. The experiment results on real dataset show that though text data structures are complex, our model can still find reliable answers compared with retrieval-based and state-of-the-art approaches.

An autoencoder is trained to generate the background from the surveillance image by setting the training label as the shuffled input, instead of the input itself in a traditional autoencoder. Then the multi-scale features are extracted by a sparse autoencoder from the surveillance image and the corresponding background to detect foreground.

We propose a novel topology control scheme that reduces the transmission power of nodes and increases the network connectivity, based on the fact that Cooperative Communication (CC) technology can bridge disconnected networks. Simulation results demonstrate that our scheme greatly increases the connectivity for a given transmission power, compared to other topology control schemes.

Collaborative filtering with only implicit feedbacks has become a quite common scenario (e.g. purchase history, click-through log, and page visitation). This kind of feedback data only has a small portion of positive instances reflecting the user's interaction. Such characteristics pose great challenges to dealing with implicit recommendation problems. In this letter, we take full advantage of matrix factorization and relative preference to make the recommendation model more scalable and flexible. In addition, we propose to take into consideration the concept of covisitation which captures the underlying relationships between items or users. To this end, we propose the algorithm Integrated Collaborative Filtering for Implicit Feedback incorporating Covisitation (ICFIF-C) to integrate matrix factorization and collaborative ranking incorporating the covisitation of users and items simultaneously to model recommendation with implicit feedback. The experimental results show that the proposed model outperforms state-of-the-art algorithms on three standard datasets.

Compared to the traditional functional dependency (FD), the extended conditional functional dependency (CFD) has shown greater potential for detecting and repairing inconsistent data. CFDMiner is a widely used algorithm for mining constant-CFDs. But the search space of CFDMiner is too large, and there is still room for efficiency improvement. In this paper, an efficient pruning strategy is proposed to optimize the algorithm by reducing the search space. Both theoretical analysis and experiments have proved the optimized algorithm can produce the consistent results as the original CFDMiner.

Physical Unclonable Function (PUF) is a promising lightweight hardware security primitive that can extract device fingerprints for encryption or authentication. However, extracting fingerprints from either the chip or the board individually has security flaws and cannot provide hardware system-level security. This paper proposes a new Chip-PCB hybrid PUF(CPR PUF) in which Weak PUF on PCB is combined with Strong PUF inside the chip to generate massive responses under the control of challenges of on-chip Strong PUF. This structure tightly couples the chip and PCB into an inseparable and unclonable unit thus can verify the authenticity of chip as well as the board. To improve the uniformity and reliability of Chip-PCB hybrid PUF, we propose a lightweight key generator based on a reliability self-test and debiasing algorithm to extract massive stable and secure keys from unreliable and biased PUF responses, which eliminates expensive error correction processes. The FPGA-based test results show that the PUF responses after robust extraction and debiasing achieve high uniqueness, reliability, uniformity and anti-counterfeiting features. Moreover, the key generator greatly reduces the execution cost and the bit error rate of the keys is less than 10-9, the overall security of the key is also improved by eliminating the entropy leakage of helper data.

The configurable Ring oscillator Physical unclonable function (CRO PUF) is the newly proposed strong PUF based on classic RO PUF, which can generate exponential Challenge-Response Pairs (CRPs) and has good uniqueness and reliability. However, existing proposals have low hardware utilization and vulnerability to modeling attacks. In this paper, we propose a Novel Configurable Dual State (CDS) PUF with lower overhead and higher resistance to modeling attacks. This structure can be flexibly transformed into RO PUF and TERO PUF in the same topology according to the parity of the Hamming Weight (HW) of the challenge, which can achieve 100% utilization of the inverters and improve the efficiency of hardware utilization. A feedback obfuscation mechanism (FOM) is also proposed, which uses the stable count value of the ring oscillator in the PUF as the updated mask to confuse and hide the original challenge, significantly improving the effect of resisting modeling attacks. The proposed FOM-CDS PUF is analyzed by building a mathematical model and finally implemented on Xilinx Artix-7 FPGA, the test results show that the FOM-CDS PUF can effectively resist several popular modeling attack methods and the prediction accuracy is below 60%. Meanwhile it shows that the FOM-CDS PUF has good performance with uniformity, Bit Error Rate at different temperatures, Bit Error Rate at different voltages and uniqueness of 53.68%, 7.91%, 5.64% and 50.33% respectively.