Transient execution attacks triggered by CPU exceptions, such as Meltdown and MDS-type attacks, have compromised system security. Unfortunately, no prior work has conducted a formalized analysis of the CPU exception transient execution security.

The row-oriented store model is the cornerstone component of modern online transaction processing (OLTP) database systems. In response to the massive increase in data within database systems, compression techniques are employed to enhance storage efficiency. Regrettably, current compression methods suffer from either the amplification issue due to coarse compression granularity or inefficient decompression operations, thus usually decreasing the speed of query processing. To this end, we present DPTC, a cost-effective and decompression-transparent approach designed to compress data pages, the basic storage unit of OLTP database systems. Specifically, (1) DPTC applies a row-wise decompression-oriented structure to track the first occurrence of redundant data in compressed data, which effectively supports the decompression of individual records from pages, thereby avoiding unwarranted decompression in record access. Moreover, (2) DPTC employs an in-page dynamic packing strategy, which determines the compression units based on the impact of each data reduction operation on the compression gains and eliminates gains-inefficient data reductions. Furthermore, (3) DPTC utilizes a SIMD-based mechanism that leverages the characteristics of operations within the decompression process to improve the decompression speed. Our evaluation results confirm that DPTC is efficient in terms of decompression speed and compression ratio. Within an OLTP database system, DPTC yields throughput improvements of up to 4.28 x in TPC-C and reduces latency by up to 33.3% for data point queries in a row-oriented storage engine.

This article aims to solve the video object segmentation (VOS) task in a scribble-supervised manner, in which VOS models are not only initialized with sparse target scribbles for inference but also trained by sparse scribble annotations. Thus, the annotation burdens for both initialization and training can be substantially lightened. The difficulties of scribble-supervised VOS lie in two aspects: 1) it demands a strong reasoning ability to carefully segment the target given only a sparse initial target scribble and 2) it necessitates learning dense prediction from sparse scribble annotations during training, requiring powerful learning capability. In this work, we propose a reliability-guided hierarchical memory network (RHMNet) for this task, which segments the target in a stepwise expanding strategy w.r.t. the memory reliability level. To be specific, RHMNet maintains a reliability-guided memory bank. It first uses the high-reliability memory to locate the region with high reliability belonging to the target, i.e., highly similar to the initial target scribble. Then, it expands the located high-reliability region to the entire target conditioned on the region itself and all existing memories. In addition, we propose a scribble-supervised learning mechanism to facilitate the model learning for dense prediction. It exploits the pixel-level relations within a single frame and the instance-level variations across multiple frames to take full advantage of the scribble annotations in sequence training samples. The favorable performance on four popular benchmarks demonstrates that our method is promising. Our project is available at: https://github.com/mkg1204/RHMNet-for-SSVOS.

Multimodal Emotion Recognition in Conversations (ERC) aims to identify the emotions conveyed by each utterance in a conversational video. Current efforts encounter challenges in balancing intra- and inter-speaker context dependencies when tackling intra-modal interactions. This balance is vital as it encompasses modeling self-dependency (emotional inertia) where speakers' own emotions affect them and modeling interpersonal dependencies (empathy) where counterparts' emotions influence a speaker. Furthermore, challenges arise in addressing cross-modal interactions that involve content with conflicting emotions across different modalities. To address this issue, we introduce an adaptive interactive graph network (IGN) called AdaIGN that employs the Gumbel Softmax trick to adaptively select nodes and edges, enhancing intra- and cross-modal interactions. Unlike undirected graphs, we use a directed IGN to prevent future utterances from impacting the current one. Next, we propose Node- and Edge-level Selection Policies (NESP) to guide node and edge selection, along with a Graph-Level Selection Policy (GSP) to integrate the utterance representation from original IGN and NESP-enhanced IGN. Moreover, we design a task-specific loss function that prioritizes text modality and intra-speaker context selection. To reduce computational complexity, we use pre-defined pseudo labels through self-supervised methods to mask unnecessary utterance nodes for selection. Experimental results show that AdaIGN outperforms state-of-the-art methods on two popular datasets. Our code will be available at https://github.com/TuGengs/AdaIGN.

Image clustering is one of the crucial techniques in multimedia analytics and knowledge discovery. Recently, the Deep clustering method (DC), characterized by its ability to perform feature learning and cluster assignment jointly, surpasses the performance of traditional ones on image data. However, existing methods rarely consider the role of model learning strategies in improving the robustness and performance of clustering complex image data. Furthermore, most approaches rely solely on point-to-point distances to cluster centers for partitioning the latent representations, resulting in error accumulation throughout the iterative process. In this paper, we propose a robust image clustering method (IDCL) which, to our knowledge for the first time, introduces a model training strategy using density information into image clustering. Specifically, we design a curriculum learning scheme grounded in the density information of input data, with a more reasonable learning pace. Moreover, we employ the density core rather than the individual cluster center to guide the cluster assignment. Finally, extensive comparisons with state-of-the-art clustering approaches on benchmark datasets demonstrate the superiority of the proposed method, including robustness, rapid convergence, and flexibility in terms of data scale, number of clusters, and image context.

The Flexible Job-shop Scheduling Problem (FJSP) is essential in today's industrial manufacturing, as it can greatly enhance efficiency in production via real-time data processing. In FJSP, it is important to consider various constraints due to the complexity of real-world production environments. Traditional Meta-heuristic methods encounter challenges in accommodating intricate problem constraints and suffer from high computational complexity, while rule-based methods perform poorly. Single-agent deep reinforcement learning frameworks are not equipped to handle complex real-world production issues. On the other hand, although existing multi-agent deep reinforcement learning frameworks designed for FJSP can solve FJSP with various constraints through minor adjustments, they often lack coordination among agents, leading to inefficient performance and unstable training. In this paper, we design a modular and coordinated multi-agent Deep Reinforcement Learning (DRL) framework that can solve FJSP problems with various constraints by adding agents tailored to specific constraints. We introduce a novel multi-agent coordinated proximal policy optimization algorithm (MACPPO), which promotes cooperation among agents by achieving dynamic credit allocation. We conducted experiments on multiple-sized instances under various constraints including equipment calendars and transportation, to verify the superiority of our framework. Experimental results show the effectiveness of the proposed novel method in addressing both the original FJSP problem and the FJSP problem with transportation and equipment calendar constraints. This efficiency is achieved compared to other well-known scheduling approaches, demonstrating the flexibility and efficiency of the architecture we proposed under diverse constraints.

Recent research has unveiled numerous cache-timing side-channel attacks exploiting the side effects of fine-grained cache features, such as coherence protocol and prefetch, among others. Traditional modeling methods and verification techniques are insufficient for verifying caches with fine-grained features and detecting cache timing vulnerabilities. There is a necessity for comprehensive verification of such complex cache designs. This paper presents SCAFinder, a verification framework targeting the cache designs with fine-grained features; it identifies cache side-channel attacks through model checking techniques. Specifically, it proposes a modeling methodology for cache designs that enables us to abstract the cache’s behavior and latency characteristics. We implement a search algorithm for finding all counterexamples based on open-source model checking software. Subsequently, we add an attack scenario analysis module to discover attacks applicable to specific scenarios. We evaluate SCAFinder on Intel Skylake-X microarchitecture, demonstrating its capability to generate 7 new attack sequences exploiting coherence protocol and prefetch, and 12 new replacement policy-based side channels. As a case study, we successfully built a covert channel for one of the sequences on the real-world processor. To the best of our knowledge, we are the first to implement cross-core replacement policy-based attacks on non-inclusive caches.

This paper proposes the distance- and velocity-based simultaneous obstacle avoidance and target tracking (DV-SOATT) method for the trajectory tracking problem of multiple wheeled mobile robots (MWMRs) operating in a shared workspace based on the relative positions and velocities of the wheeled mobile robots (WMRs) and their encountered obstacles. Compared to the previous arts considered only their relative positions, the DV-SOATT method that adds an auxiliary velocity vector lessens needless activation of the collision avoidance maneuvers, where the DV-SOATT introduces radial bounds for forecasting a collision. We provide two decision criteria for the addition of the auxiliary velocity term and compare the DV-SOATT method with the original method proposed by Li et al. (2021). The problem of the WMRs pause from the path conflict is addressed. Bound constraints on MWMRs’ velocities are considered to restrict the movement speed of the robot so as to ensure smoothness. The control law is built on Lagrange multipliers on basis of constructing a quadratic programming problem. Slack variables are discarded. Bound constraints on optimization variables are included in the piecewise-linear projection function. The stability of the control law, together with the efficiency of the DV-SOATT method, is discussed based on the Lyapunov function. The efficiency is tested on multiple omnidirectional Mecanum-wheeled mobile robots and validated through physical experiments and simulation.

With the recent advancement of deep neural networks, visual tracking has achieved substantial progress in tracking accuracy. However, the robustness and security of tracking methods developed based on current deep models have not been thoroughly explored, a critical consideration for real-world applications. In this study, we propose a context-guided black-box attack method to investigate the robustness of recent advanced deep trackers against spatial and temporal interference. For spatial interference, the proposed algorithm generates adversarial target samples by mixing the information of the target object and the similar background regions around it in an embedded feature space of an encoder-decoder model, which evaluates the ability of trackers to handle background distractors. For temporal interference, we use the target state in the previous frame to generate the adversarial sample, which easily fools the trackers that rely too heavily on tracking prior assumptions, such as that the appearance changes and movements of a video target object are small between two consecutive frames. We assess the proposed attack method under both CNN-based and transformer-based tracking frameworks on four diverse datasets: OTB100, VOT2018, GOT-10k, and LaSOT. The experimental results demonstrate that our approach substantially deteriorates the performance of all these deep trackers across numerous datasets, even in the black-box attack mode. This reveals the weak robustness of recent deep tracking methods against background distractors and prior dependencies.

Image clustering is a crucial but challenging task in multimedia machine learning. Recently the combination of clustering with deep learning has achieved promising performance against conventional methods on high-dimensional image data. Unfortunately, existing deep clustering methods (DC) often ignore the importance of information fusion with a global perception field among different image regions for clustering images, especially complex ones. Additionally, the learned features are usually not clustering-friendly in terms of dimensionality and are based only on simple distance information for the clustering. In this regard, we propose a deep embedded image clustering TDEC, which for the first time to our knowledge, jointly considers feature representation, dimensional preference, and robust assignment for image clustering. Specifically, we introduce the Transformer to form a novel module T-Encoder to learn discriminative features with global dependency while using the Dim-Reduction block to build a friendly low-dimensional space favoring clustering. Moreover, the distribution information of embedded features is considered in the clustering process to provide reliable supervised signals for joint training. Our method is robust and allows for more flexibility in data size, the number of clusters, and the context complexity. More importantly, the clustering performance of TDEC is much higher than that of recent competitors. Extensive experiments with state-of-the-art approaches on complex datasets demonstrate the superiority of TDEC.

The crux of long-term tracking lies in the difficulty of tracking the target with discontinuous moving caused by out-of-view or occlusion. Existing long-term tracking methods follow two typical strategies. The first strategy employs a local tracker to perform smooth tracking and uses another re-detector to detect the target when the target is lost. While it can exploit the temporal context like historical appearances and locations of the target, a potential limitation of such strategy is that the local tracker tends to misidentify a nearby distractor as the target instead of activating the re-detector when the real target is out of view. The other long-term tracking strategy tracks the target in the entire image globally instead of local tracking based on the previous tracking results. Unfortunately, such global tracking strategy cannot leverage the temporal context effectively. In this work, we combine the advantages of both strategies: tracking the target in a global view while exploiting the temporal context. Specifically, we perform global tracking via ensemble of local trackers spreading the full image. The smooth moving of the target can be handled steadily by one local tracker. When the local tracker accidentally loses the target due to suddenly discontinuous moving, another local tracker close to the target is then activated and can readily take over the tracking to locate the target. While the activated local tracker performs tracking locally by leveraging the temporal context, the ensemble of local trackers renders our model the global view for tracking. Extensive experiments on six datasets demonstrate that our method performs favorably against state-of-the-art algorithms.

Trackers based on the IoU prediction network (IoU-Net) have shown superior performance, which refines a coarse bounding box to an accurate one by maximizing the IoU between the target and the coarse box. However, the traditional IoU-Net is less effective in exploiting the limited but crucial supervision information contained in the initial frame, including the discriminative information between the target and backgrounds and the structure information of the initial target. Missing such information makes the IoU-Net less robust to background distractors and diverse variations of the target appearance. To address this issue, we propose a target-aware state estimation network for visual tracking. A gradient-guided feature adjustment module is built on an online discriminative model to generate target-aware features for constructing the state estimation network; it conveys the online learned discriminative information into the offline trained state estimation network. In addition, we propose a structure-aware integration module and embed it into the state estimation network, enabling the tracker to explicitly model the structure information of the initial target. Extensive experimental results on the VOT2018, OTB2015, UAV123, NFS30, TC128, TrackingNet, LaSOT, and VOT2018-LT datasets demonstrate that the proposed approach performs favorably against state-of-the-art trackers.

Temporal and spatial contexts, characterizing target appearance variations and target-background differences, respectively, are crucial for improving the online adaptive ability and instance-level discriminative ability of object tracking. However, most existing trackers focus on either the temporal context or the spatial context during tracking and have not exploited these contexts simultaneously and effectively. In this paper, we propose a Spatial-TEmporal Memory (STEM) network to exploit these contexts jointly for object tracking. Specifically, we develop a key-value structured memory model equipped with a key-value index-based memory reading mechanism to model the spatial and temporal contexts simultaneously. To update the memory with new target states and ensure the diversity of the memory, we introduce a similarity-aware memory update scheme. In addition, we construct an entropy-guided ensemble strategy to fuse the prediction models based on these two contexts, such that these two contexts can be exploited to estimate the target state jointly. Extensive experimental results on eight challenging datasets, including OTB2015, TC128, UAV123, VOT2018, LaSOT, TrackingNet, GOT-10k, and OxUvA, demonstrate that the proposed method performs favorably against state-of-the-art trackers.

As a research hotspot of computer vision, crowd counting methods have achieved success in natural images. But crowd counting in aerial images are rarely explored, and existing methods do not perform well because of the higher resolution, smaller object scale and more complex scene. Therefore, this paper proposes a lightweight dual-task network (LDNet) for crowd counting, which only uses bifurcated structure to overcome these new challenges in aerial images without complicated pipelines. To realize this, a complete but efficient Guidance Branch is proposed to assist Counting Branch in fitting crowd distribution. Furthermore, a scene attention mechanism is used to consider the complex scene information, which are never considered by existing methods. Our LD-Net outperforms existing methods on aerial crowd counting dataset (Visdrone), and gets better or comparable results on natural crowd counting datasets (UCF_CC_50, UCF_QNRF, ShanghaiTech Part A).

Most existing trackers based on deep learning perform tracking in a holistic strategy, which aims to learn deep representations of the whole target for localizing the target. It is arduous for such methods to track targets with various appearance variations. To address this limitation, another type of methods adopts a part-based tracking strategy which divides the target into equal patches and tracks all these patches in parallel. The target state is inferred by summarizing the tracking results of these patches. A potential limitation of such trackers is that not all patches are equally informative for tracking. Some patches that are not discriminative may have adverse effects. In this paper, we propose to track the salient local parts of the target that are discriminative for tracking. In particular, we propose a fine-grained saliency mining module to capture the local saliencies. Further, we design a saliency-association modeling module to associate the captured saliencies together to learn effective correlation representations between the exemplar and the search image for state estimation. Extensive experiments on five diverse datasets demonstrate that the proposed method performs favorably against state-of-the-art trackers.

Existing deep Thermal InfraRed (TIR) trackers only use semantic features to represent the TIR object, which lack the sufficient discriminative capacity for handling distractors. This becomes worse when the feature extraction network is only trained on RGB images. To address this issue, we propose a multi-level similarity model under a Siamese framework for robust TIR object tracking. Specifically, we compute different pattern similarities using the proposed multi-level similarity network. One of them focuses on the global semantic similarity and the other computes the local structural similarity of the TIR object. These two similarities complement each other and hence enhance the discriminative capacity of the network for handling distractors. In addition, we design a simple while effective relative entropy based ensemble subnetwork to integrate the semantic and structural similarities. This subnetwork can adaptive learn the weights of the semantic and structural similarities at the training stage. To further enhance the discriminative capacity of the tracker, we propose a large-scale TIR video sequence dataset for training the proposed model. To the best of our knowledge, this is the first and the largest TIR object tracking training dataset to date. The proposed TIR dataset not only benefits the training for TIR object tracking but also can be applied to numerous TIR visual tasks. Extensive experimental results on three benchmarks demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.

Most past works for DNA-binding residue prediction did not consider the relationships between residues. In this paper, we propose a novel approach for DNA-binding residue prediction, referred to as EL_LSTM, which includes two main components. The first component is the Long Short-Term Memory (LSTM), which learns pairwise relationships between residues through a bi-gram model and then learns feature vectors for all residues. The second component is an ensemble learning based classifier introduced to tackle the data imbalance problem in binding residue predictions. We use a variant of the bagging strategy in ensemble learning to achieve balanced samples. Evaluations on PDNA-224 and DBP-123 show that adding feature relationships performs better than classifiers without feature relationships by at least 0.028 on MCC, 1.18 percent on ST and 0.012 on AUC. This indicates the usefulness of feature relationships for DNA-binding residue predictions. Evaluation on using ensemble learning indicates that the improvement can reach at least 0.021 on MCC, 1.32 percent on ST, and 0.018 on AUC compared to the use of a single LSTM classifier. Comparisons with the state-of-the-art predictors show that our proposed EL_LSTM outperforms them significantly. Further feature analysis validates the effectiveness of LSTM for the prediction of DNA-binding residues.

Most proposed methods for TF-binding site (TFBS) predictions only use low order dependencies for predictions due to the lack of efficient methods to extract higher order dependencies. In this work, we first propose a novel method to extract higher order dependencies by applying CNN on histone modification features. We then propose a novel TFBS prediction method, referred to as CNN_TF, by incorporating low order and higher order dependencies. CNN_TF is first evaluated on 13 TFs in the mES cell. Results show that using higher order dependencies outperforms low order dependencies significantly on 11 TFs. This indicates that higher order dependencies are indeed more effective for TFBS predictions than low order dependencies. Further experiments show that using both low order dependencies and higher order dependencies improves performance significantly on 12 TFs, indicating the two dependency types are complementary. To evaluate the influence of cell-types on prediction performances, CNN_TF was applied to five TFs in five cell-types of humans. Even though low order dependencies and higher order dependencies show different contributions in different cell-types, they are always complementary in predictions. When comparing to several state-of-the-art methods, CNN_TF outperforms them by at least 5.3 percent in AUPR.

MOTIVATION: The prediction of transcription factor binding sites (TFBSs) is crucial for gene expression analysis. Supervised learning approaches for TFBS predictions require large amounts of labeled data. However, many TFs of certain cell types either do not have sufficient labeled data or do not have any labeled data. RESULTS: In this paper, a multi-task learning framework (called MTTFsite) is proposed to address the lack of labeled data problem by leveraging on labeled data available in cross-cell types. The proposed MTTFsite contains a shared CNN to learn common features for all cell types and a private CNN for each cell type to learn private features. The common features are aimed to help predicting TFBSs for all cell types especially those cell types that lack labeled data. MTTFsite is evaluated on 241 cell type TF pairs and compared with a baseline method without using any multi-task learning model and a fully shared multi-task model that uses only a shared CNN and do not use private CNNs. For cell types with insufficient labeled data, results show that MTTFsite performs better than the baseline method and the fully shared model on more than 89% pairs. For cell types without any labeled data, MTTFsite outperforms the baseline method and the fully shared model by more than 80 and 93% pairs, respectively. A novel gene expression prediction method (called TFChrome) using both MTTFsite and histone modification features is also presented. Results show that TFBSs predicted by MTTFsite alone can achieve good performance. When MTTFsite is combined with histone modification features, a significant 5.7% performance improvement is obtained. AVAILABILITY AND IMPLEMENTATION: The resource and executable code are freely available at http://hlt.hitsz.edu.cn/MTTFsite/ and http://www.hitsz-hlt.com:8080/MTTFsite/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Protein secondary structure is the three dimensional form of local segments of proteins and its prediction is an important problem in protein tertiary structure prediction. Developing computational approaches for protein secondary structure prediction is becoming increasingly urgent. RESULTS: We present a novel deep learning based model, referred to as CNNH_PSS, by using multi-scale CNN with highway. In CNNH_PSS, any two neighbor convolutional layers have a highway to deliver information from current layer to the output of the next one to keep local contexts. As lower layers extract local context while higher layers extract long-range interdependencies, the highways between neighbor layers allow CNNH_PSS to have ability to extract both local contexts and long-range interdependencies. We evaluate CNNH_PSS on two commonly used datasets: CB6133 and CB513. CNNH_PSS outperforms the multi-scale CNN without highway by at least 0.010 Q8 accuracy and also performs better than CNF, DeepCNF and SSpro8, which cannot extract long-range interdependencies, by at least 0.020 Q8 accuracy, demonstrating that both local contexts and long-range interdependencies are indeed useful for prediction. Furthermore, CNNH_PSS also performs better than GSM and DCRNN which need extra complex model to extract long-range interdependencies. It demonstrates that CNNH_PSS not only cost less computer resource, but also achieves better predicting performance. CONCLUSION: CNNH_PSS have ability to extracts both local contexts and long-range interdependencies by combing multi-scale CNN and highway network. The evaluations on common datasets and comparisons with state-of-the-art methods indicate that CNNH_PSS is an useful and efficient tool for protein secondary structure prediction.

BACKGROUND: Prediction of DNA-binding residue is important for understanding the protein-DNA recognition mechanism. Many computational methods have been proposed for the prediction, but most of them do not consider the relationships of evolutionary information between residues. RESULTS: In this paper, we first propose a novel residue encoding method, referred to as the Position Specific Score Matrix (PSSM) Relation Transformation (PSSM-RT), to encode residues by utilizing the relationships of evolutionary information between residues. PDNA-62 and PDNA-224 are used to evaluate PSSM-RT and two existing PSSM encoding methods by five-fold cross-validation. Performance evaluations indicate that PSSM-RT is more effective than previous methods. This validates the point that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction. An ensemble learning classifier (EL_PSSM-RT) is also proposed by combining ensemble learning model and PSSM-RT to better handle the imbalance between binding and non-binding residues in datasets. EL_PSSM-RT is evaluated by five-fold cross-validation using PDNA-62 and PDNA-224 as well as two independent datasets TS-72 and TS-61. Performance comparisons with existing predictors on the four datasets demonstrate that EL_PSSM-RT is the best-performing method among all the predicting methods with improvement between 0.02-0.07 for MCC, 4.18-21.47% for ST and 0.013-0.131 for AUC. Furthermore, we analyze the importance of the pair-relationships extracted by PSSM-RT and the results validates the usefulness of PSSM-RT for encoding DNA-binding residues. CONCLUSIONS: We propose a novel prediction method for the prediction of DNA-binding residue with the inclusion of relationship of evolutionary information and ensemble learning. Performance evaluation shows that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction and ensemble learning can be used to address the data imbalance issue between binding and non-binding residues. A web service of EL_PSSM-RT ( http://hlt.hitsz.edu.cn:8080/PSSM-RT_SVM/ ) is provided for free access to the biological research community.

A good skin detector that is capable of capturing skin tones under different conditions is important for human-machine interaction applications. In a general situation, skin detectors, such as skin probability maps or Gaussian mixture models, achieve acceptable skin segmentation results. However, the false positive rate increases significantly when the skin tones are in shadow or when skin-like background objects are under similar illumination. In this paper, we propose a novel skin feature learning algorithm based on stacked autoencoders, which are deep neural networks. To overcome the problems encountered in skin segmentation that are caused by different ethnicities and varying illumination conditions, the stacked autoencoders are utilized to learn more discriminative representations of the skin area in both the RGB color space and the HSV color space. Unlike traditional machine learning methods, instead of predicting each pixel individually, our algorithm utilizes blocks to learn the representations and detect the skin areas. The algorithm exploits the learning ability of deep neural networks to learn high-level representations of skin tones. Experiments on test images show that the proposed algorithm achieves acceptable results on several publicly available data sets. To reduce the difficulty of detecting skin pixels in these data sets, the ground truths of these data sets are commonly focused on foreground skin area detection. Our skin detector is also able to detect background areas, as shown in our experiments.

Protein-DNA complexes play crucial roles in gene regulation. The prediction of the residues involved in protein-DNA interactions is critical for understanding gene regulation. Although many methods have been proposed, most of them overlooked motif features. Motif features are sub sequences and are important for the recognition between a protein and DNA. In order to efficiently use motif features for the prediction of DNA-binding residues, we first apply the Convolutional Neural Network (CNN) method to capture the motif features from the sequences around the target residues. CNN modeling consists of a set of learnable motif detectors that can capture the important motif features by scanning the sequences around the target residues. Then we use a neural network classifier, referred to as CNNsite, by combining the captured motif features, sequence features and evolutionary features to predict binding residues from sequences. The datasets PDNA-62 and PDNA-224 are used to evaluate the performance of CNNsite by five-fold cross-validation. Performance evaluation shows that the motif features performs better than sequence features and evolutionary features with at least 6.73% on ST, 0.097 on MCC and 0.069 on AUC. When comparing with previously published methods, CNNsite performs better with at least 0.019 on MCC, 4.37% on ST and 0.040 on AUC. CNNsite is also evaluated on an independent dataset TS-72 and CNNsite outperforms the previous methods by at least 0.012 on AUC. The discriminant powers of the motif features of size from 2 to 6 residues show that many motif features with large discriminant power are composed by the residues that play important roles in the DNA-protein interactions. The standalone version of the CNNsite is available at http://hlt.hitsz.edu.cn:8080/CNNsite/.