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Many existing solutions perform document-level sentiment classification based on local document only, ignoring other texts that might contribute to better classification accuracy. In this paper, we propose a novel speculative sentiment classification model named SSC. In SSC, we speculate that users with similar rating behaviours are more likely to write documents of similar sentiments toward a product. The motivation of SSC, therefore, is to exploit those speculative similar documents for improving classification accuracy. The proposed SSC model consists of three main components, namely, user-product interaction (UPI) component, document encoding (DE) component, and speculative similar document (SSD) component. The UPI component models user-product interactions, and encodes user/product ratings behaviours into user/product embeddings. The DE component utilizes learned user/product embeddings to capture the informative word vectors for comprising more accurate document representations. The SSD component aggregates documents written by similar users toward the same product for speculative sentiment classification. Because the user similarities are calculated based on user embeddings that encode user rating behaviours, the aggregated documents are more likely to have similar sentiments. The three components are seamlessly integrated into a unified model. In the unified manner, these three components are jointly optimized, and they mutually complement each other to enhance sentiment classification. We conduct extensive experiments on three public datasets, and demonstrate the advantage of the proposed SSC model over state-of-the-art baselines.

In this study, Ulva lactuca polysaccharide (ULP) antihyperglycemic effect was assessed by monitoring changes in the gut microbiota of aging diabetic mice. The results showed that ULP alleviated type 2 diabetes by improving insulin tolerance, increasing SOD and CAT activities, and thus lowering blood glucose level. Moreover, ULP regulated the expressions of INSR and AMPK concurrent with inhibition the expression of JNK, JAK, STAT3, p16 and p38 to improve glucose metabolism dysfunction. Interestingly, the abundance of Alloprevotella and Pediococcus change might the key factor for ULP antihyperglycemic effectiveness in aging-related diabetes. These results suggest that ULP can exert a mechanism of blood glucose regulation by improving intestinal diversity composition asides from direct insulin mimetic actions.

In this paper, we propose solutions to advance answer selection in Community Question Answering (CQA). Automatically selecting correct answers can significantly improve intelligence for CQA, as users are not required to browse the large quantity of texts and select the right answers manually. Also, automatic answers selection can minimize the time for satisfying users seeking the correct answers and maximize user engagement with the site. Unlike previous works, we propose a hybrid attention mechanism to model question-answer pairs. Specifically, for each word, we calculate the intra-sentence attention indicating its local importance and the inter-sentence attention implying its importance to the counterpart sentence. The inter-sentence attention is based on the interactions between question-answer pairs, and the combination of these two attention mechanisms enables us to align the most informative parts in question-answer pairs for sentence matching. Additionally, we exploit user information for answer selection due to the fact that users are more likely to provide correct answers in their areas of expertise. We model users from their written answers to alleviate data sparsity problem, and then learn user representations according to the informative parts in sentences that are useful for question-answer matching task. This mean of modelling users can bridge the semantic gap between different users, as similar users may have the same way of wording their answers. The representations of users, questions and answers are learnt in an end-to-end neural network in a mean that best explains the interrelation between question-answer pairs. We validate the proposed model on a public dataset, and demonstrate its advantages over the baselines with thorough experiments.

Money is the most common medium of exchange and plays an important role in our daily life. However, current literature has not yet specifically touched on the influence of money priming on decision-making behaviour under uncertainty and related neural mechanisms. In this study, we used event-related potentials with an adapted version of the Balloon Analogue Risk Task (BART) paradigm to examine brain activity related to the effects of money priming on outcome evaluation in decision-making under uncertainty. Reward positivity (RewP) and P300 components were analysed with respect to feedback valence (win vs. loss) and priming condition (money vs. neutral). The ERP results demonstrated that when individuals made decisions after having been primed with the monetary concept, the positive outcome feedback evoked a larger RewP component than after they had been primed with neutral stimuli. Conversely, there was no significant money priming effect when the outcome feedback was negative. In contrast, when individuals made decisions after having been primed with the monetary concept, the negative outcome feedback evoked a larger P300 than after they had been primed with neutral stimuli, whereas there was no significant money priming effect when the outcome feedback was positive. Our findings, thus, indicate that the brain response to money priming effects on the outcome evaluation in the BART occurs at both an early semi-automatic processing stage and a later cognitive appraisal stage. They further suggest that individuals prefer achieving financial gains at first and then focus on preventing financial losses in the money priming condition relative to the neutral priming condition.

Smart hydrogels are regarded as novel sensors due to large reversible volume change in response to external stimuli. Here we prepared a novel visual multi-triggered sensor based on inverse opal hydrogel (IOH) that responses to pH, temperature and light, respectively. The hydrogel was composed of hydrophilic pH and temperature responsive poly(dimethylaminoethyl methacrylate) (PDMAEMA), and light responsive spiropyran-containing segment. When the pH decreased, the sensor changed its structural color from blue to orange-red, and the structural color was reversibly recovered by increasing pH. In addition, the color of sensor can be controlled to change from green to violet while applying a temperature field. Furthermore, the reflection peak of IOH exhibited obvious shifted behavior by UV/visible irradiation. Repeated condition changes revealed that the sensor has a long lifetime.

Pyrophosphate (P2O74−, PPi) plays a vital role in ecological environment. Its elevated levels in water bodies can lead to eutrophication. Hence, its detection is extremely significant. Whereas most of the existing methods for the actual detection of PPi may cause environmental pollution or suffer from operational complexity. In this study, we introduced a sensitive and selective method for detecting PPi based on the fact that PPi can inhibit the peroxidase-like activity of adenosine 5′-triphosphate (ATP). This strategy not only eliminated the complexity of material preparation (ATP is commercialized), but also addressed the general need for metal ions in detecting PPi. The dynamic range of PPi detection was 1.0–200 μM and the detection limit was 74 nM. In addition, this strategy had been successfully applied to the determination of PPi in tap water and lake water. This work extends the application of natural biological small molecule ATP in the analysis and provides an innovative thought for the metal-free detection of PPi.

A general and efficient way for enlarging the absorption wavelength of optical materials with high molar absorptivity was explored by incorporating a hydroxyl group ortho-position to a bridging group between the electron-withdrawing and the electron-donating groups. Both theoretical deduction and experimental data indicated that the molecular design strategy was rational and efficacious. Comparing to its analogy, the target materials, 3-hydroxyl-4-(N- ethyl-N-ethoxyl phenylazo) naphthalene sulfonic acid (HPNSA), possessed the improved optical property by extending the intrinsic D-π-A conjugated structure and selective recognition to Fe2+ by forming stable 5-member rings. Importantly, the proposed material was effectively applied for colorimetric determination of Fe2+ in four real environmental water samples with an accurate deviation of 4.0% and a low detection limit of 38 nmol L−1. The action mechanism between HPNSA and Fe2+ was investigated in detail.

Common use of smartphones is a compelling reason for performing activity recognition with on-board sensors as it is more practical than other approaches, such as wearable sensors and augmented environments. Many solutions have been proposed by academia, but practical use is limited to experimental settings. Ad hoc solutions exist with different degrees in recognition accuracy and efficiency. To ease the development of activity recognition for the mobile application eco-system, Google released an activity recognition service on their Android platform. In this paper, we present a systematic evaluation of this activity recognition service and share the lesson learnt. Through our experiments, we identified scenarios in which the recognition accuracy was barely acceptable. We analyze the cause of the inaccuracy and propose four practical and light-weight solutions to significantly improve the recognition accuracy and efficiency. Our evaluation confirmed the improvement. As a contribution, we released the proposed solutions as open-source projects for developers who want to incorporate activity recognition into their applications.

We demonstrate that different people perform activities differently.Combine AdaBoost with LDA to build general activity model with minimum labelled data.Hybrid AdaBoost with HMM&CRF for temporal regulatization of human activities.Use publicly available datasets to validate the proposed methods. Activity recognition has been a hot topic for decades, from the scientific research to the development of off-the-shelf commercial products. Since people perform the activities differently, to avoid overfitting, building a general model with activity data of various users is required before the deployment for personal use. However, annotating a large amount of activity data is expensive and time-consuming. In this paper, we build a general model for activity recognition with a limited amount of labelled data. We combine Latent Dirichlet Allocation (LDA) and AdaBoost to jointly train a general activity model with partially labelled data. After that, when AdaBoost is used for online prediction, we combine it with graphical models (such as HMM and CRF) to exploit the temporal information in human activities to smooth out the accidental misclassifications. Experiments with publicly available datasets show that we are able to obtain the accuracy of more than 90% with 1% labelled data.

Aspect Terms Extraction (ATE) and Aspect Categories Detection (ACD) are two fundamental sub-tasks for aspect-based sentiment analysis. Most of the existing works mainly focus on the ATE task or the co-extraction of aspect terms and opinion words, while few attention are paid to the ACD task. In this work, we propose a joint model to seamlessly integrate the ATE and ACD tasks into a multi-task learning framework. Each of the tasks is based on multi-layer Convolutional Neural Networks (CNNs) for computing high-level word representations, and produces a task-specific and a task-share vector. The task-share vector of one task is used to propagate information to the other, and guides the counterpart task to align the informative textual features to produce the task-specific vectors. Finally, a fully-connected layer with a softmax/sigmoid function is applied to the task-specific vectors for the specific information extraction. The rationale underlying the proposed joint model is that, aspect terms and aspect categories are semantically related, and the information propagated between the two tasks can help to capture the semantic alignments between the aspect terms and categories, and produce informative task-specific vectors. Moreover, the ATE task models local semantics at each position of a sentence, while the ACD task extracts global features of the whole sentence. The mutual interactions between local and global features, therefore, can reciprocally capture informative textual features for the information extraction tasks. We validate the effectiveness of the proposed model on two widely used datasets, and show its advantage over the state-of-the-art baselines. We also investigate the effectiveness of the multi-task framework by comparing the proposed model with its variants. Further, we study the robustness of the proposed model by presenting the model performance with respect to different hyperparameters. Finally, we provide visualization examples to gain a better understanding of the advantages the multi-task learning scheme.

An activity recognition system essentially processes raw sensor data and maps them into latent activity classes. Most of the previous systems are built with supervised learning techniques and pre-defined data sources, and result in static models. However, in realistic and dynamic environments, original data sources may fail and new data sources become available, a robust activity recognition system should be able to perform evolution automatically with dynamic sensor availability in dynamic environments. In this paper, we propose methods that automatically incorporate dynamically available data sources to adapt and refine the recognition system at run-time. The system is built upon ensemble classifiers which can automatically choose the features with the most discriminative power. Extensive experimental results with publicly available datasets demonstrate the effectiveness of our methods. HighlightsPropose an activity recognition framework to incorporate dynamically discovered sensors automatically.Propose a method to select the most informative samples for retraining.Propose a novel way of combining AdaBoost with HMM&CRF for temporal regularization.

Neighborhood-based recommenders are a major class of Collaborative Filtering models. The intuition is to exploit neighbors with similar preferences for bridging unseen user-item pairs and alleviating data sparseness, in other words, learn the sub-graph representation of each user in a user graph. Many existing works propose neural attention networks to aggregate neighbors and place higher weights on the specific subsets of users for recommendation. However, the neighborhood information is not necessarily always informative, and the noises in the neighborhood can negatively affect the model performance. To address this issue, we propose a novel neighborhood-based recommender, where a hybrid gated network is designed to automatically separate similar neighbors from dissimilar (noisy) ones, and aggregate those similar neighbors to comprise neighborhood representations. The confidence in the neighborhood is also addressed by putting higher weights on the neighborhood representations if we are confident with the neighborhood information, and vice versa. In addition, a user-neighbor component is proposed to explicitly regularize user-neighbor proximity in latent space. These two components are combined into a unified model to complement each other for the recommendation task. Extensive experiments on three public datasets demonstrate that the proposed model consistently outperforms the state-of-the-art neighborhood-based recommenders. Furthermore, we study different variants of the proposed model to justify the underlying intuition of the proposed hybrid gated network and user-neighbor modeling components.

Traditional recommender systems encounter several challenges such as data sparsity and unexplained recommendation. To address these challenges, many works propose to exploit semantic information from review data. However, these methods have two major limitations in terms of the way to model textual features and capture textual interaction. For textual modeling, they simply concatenate all the reviews of a user/item into a single review. However, feature extraction at word/phrase level can violate the meaning of the original reviews. As for textual interaction, they defer the interactions to the prediction layer, making them fail to capture complex correlations between users and items. To address those limitations, we propose a novel Hierarchical Text Interaction model (HTI) for rating prediction. In HTI, we propose to model low-level word semantics and high-level review representations hierarchically. The hierarchy allows us to exploit textual features at different granularities. To further capture complex user–item interactions, we propose to exploit semantic correlations between each user–item pair at different hierarchies. At word level, we propose an attention mechanism specialized to each user–item pair, and capture the important words for representing each review. At review level, we mutually propagate textual features between the user and item, and capture the informative reviews. The aggregated review representations are integrated into a collaborative filtering framework for rating prediction. Experiments on five real-world datasets demonstrate that HTI outperforms state-of-the-art models by a large margin. Further case studies provide a deep insight into HTI’s ability to capture semantic correlations at different levels of granularities for rating prediction.

Fast association aims to quickly associate a large number of stations with an access point in 802.11ah networks. Existing fast association mechanisms suffer from efficiency, fairness and robustness problems. In this paper, we propose the mechanism of Fast Association based on Speculating the nUmber of Stations (FASUS). FASUS adopts new methods for retransmission, thresholding and adaptive round selection, which greatly improve the association performance. Experiments show that FASUS is able to reduce the association time by 67.1% when compared with the Linear Increase Linear Decrease (LILD) method, which is one of the best-known mechanisms for 802.11ah networks. We formulate a mathematical model to analyze the association process and to determine the optimal number of stations per round. Furthermore, we propose two methods to address the inter- and intra-network interference in congested networks. To improve the robustness and fairness of the network, we propose new solutions to address two possible attacks: 1) the Denial-of-Service (DoS) attack, which can disable the whole network, and 2) the selfish node attack, which may unfairly allow the attackers to associate in a much faster way than normal stations.

In wireless 802.11 networks, MAC-layer rate control mechanisms such as Minstrel have an important role in selecting an appropriate transmission rate to maximize throughput and/or to minimize frame loss. Transmitting at a higher rate in good channel quality achieves high throughput, but it achieves a low or zero throughput if the channel quality is bad. In many MAC-layer rate control mechanisms, the selection of transmission rates depends on link quality metrics calculated based on MAC-layer acknowledgements (ACKs) of the successful transmissions of data frames. We found that injecting forged ACKs to acknowledge lost data frames can mislead most rate control mechanisms to select an unsustainable transmission rate resulting in frame losses. Due to the lack of protection of ACKs, the sender cannot detect and avoid this attack. In this paper, we develop a MAC-layer ACK spoofing mechanism and design experiments to exploit such a vulnerability in 802.11 networks. We analyze this vulnerability in different attack models for well-known MAC-layer rate control mechanisms. Our experiment results show that the spoofing attack can reduce the throughput to zero by tricking the sender to believe the highest rate should be used even when the channel quality is bad. To address this issue, we present a defense mechanism that is effective and light-weight. Experiment results confirm the effectiveness of the proposed solution with low communication overhead.

We propose an efficient frequent activity patterns mining in smart environments.We build an accurate activity classifier based on the mined frequent patterns.We distinguish overlapped activities with global and local weights of sensor events.We use publicly available dataset of smart environments to validate our methods. In the past decades, activity recognition has aroused a great interest for the research groups majoring in context-awareness computing and human behaviours monitoring. However, the correlations between the activities and their frequent patterns have never been directly addressed by traditional activity recognition techniques. As a result, activities that trigger the same set of sensors are difficult to differentiate, even though they present different patterns such as different frequencies of the sensor events. In this paper, we propose an efficient association rule mining technique to find the association rules between the activities and their frequent patterns, and build an activity classifier based on these association rules. We also address the classification of overlapped activities by incorporating the global and local weight of the patterns. The experiment results using publicly available dataset demonstrate that our method is able to achieve better performance than traditional recognition methods such as Decision Tree, Naive Bayesian and HMM. Comparison studies show that the proposed association rule mining method is efficient, and we can further improve the activity recognition accuracy by considering global and local weight of frequent patterns of activities.

We propose an activity recognition model balancing accuracy, overhead, data labelling.We propose a similarity measurement method to effectively discover activity patterns.We perform comprehensive experimental and comparison studies to validate our method. In the past decades, activity recognition had aroused great interest for the community of context-awareness computing and human behaviours monitoring. However, most of the previous works focus on supervised methods in which the data labelling is known to be time-consuming and sometimes error-prone. In addition, due to the randomness and erratic nature of human behaviours in realistic environments, supervised models trained with data from certain subject might not be scaled to others. Further more, unsupervised methods, with little knowledge about the activities to be recognised, might result in poor performance and high clustering overhead. To this end, we propose an activity recognition model with labelled and unlabelled data in smart environments. With small amount of labelled data, we discover activity patterns from unlabelled data based on proposed similarity measurement algorithm. Our system does not require large amount of data to be labelled while the proposed similarity measurement method is effective to discover length-varying, disordered and discontinuous activity patterns in smart environments. Therefore, our methods yield comparable performance with much less labelled data when compared with traditional supervised activity recognition, and achieve higher accuracy with lower clustering overhead compared with unsupervised methods. The experiments based on real datasets from the smart environments demonstrate the effectiveness of our method, being able to discover more than 90% of original activities from the unlabelled data, and the comparative experiments show that our methods are capable of providing a better trade-off, regarding the accuracy, overhead and labelling efforts, between the supervised and unsupervised methods.