This paper proposes a kind of programmable logic element (PLE) based on Sense-Switch pFLASH technology. By programming Sense-Switch pFLASH, all three-bit look-up table (LUT3) functions, partial four-bit look-up table (LUT4) functions, latch functions, and d flip flop (DFF) with enable and reset functions can be realized. Because PLE uses a choice of operational logic (COOL) approach for the operation of logic functions, it allows any logic circuit to be implemented at any ratio of combinatorial logic to register. This intrinsic property makes it close to the basic application specific integrated circuit (ASIC) cell in terms of fine granularity, thus allowing ASIC-like cell-based mappers to apply all their optimization potential. By measuring Sense-Switch pFLASH and PLE circuits, the results show that the “on” state driving current of the Sense-Switch pFLASH is about 245.52 µA, and that the “off” state leakage current is about 0.1 pA. The programmable function of PLE works normally. The delay of the typical combinatorial logic operation AND3 is 0.69 ns, and the delay of the sequential logic operation DFF is 0.65 ns, both of which meet the requirements of the design technical index.

Precise targeting of transcription factor binding sites (TFBSs) is essential to comprehending transcriptional regulatory processes and investigating cellular function. Although several deep learning algorithms have been created to predict TFBSs, the models' intrinsic mechanisms and prediction results are difficult to explain. There is still room for improvement in prediction performance. We present DeepSTF, a unique deep-learning architecture for predicting TFBSs by integrating DNA sequence and shape profiles. We use the improved transformer encoder structure for the first time in the TFBSs prediction approach. DeepSTF extracts DNA higher-order sequence features using stacked convolutional neural networks (CNNs), whereas rich DNA shape profiles are extracted by combining improved transformer encoder structure and bidirectional long short-term memory (Bi-LSTM), and, finally, the derived higher-order sequence features and representative shape profiles are integrated into the channel dimension to achieve accurate TFBSs prediction. Experiments on 165 ENCODE chromatin immunoprecipitation sequencing (ChIP-seq) datasets show that DeepSTF considerably outperforms several state-of-the-art algorithms in predicting TFBSs, and we explain the usefulness of the transformer encoder structure and the combined strategy using sequence features and shape profiles in capturing multiple dependencies and learning essential features. In addition, this paper examines the significance of DNA shape features predicting TFBSs. The source code of DeepSTF is available at https://github.com/YuBinLab-QUST/DeepSTF/.

Interactions between DNA and transcription factors (TFs) play an essential role in understanding transcriptional regulation mechanisms and gene expression. Due to the large accumulation of training data and low expense, deep learning methods have shown huge potential in determining the specificity of TFs-DNA interactions. Convolutional network-based and self-attention network-based methods have been proposed for transcription factor binding sites (TFBSs) prediction. Convolutional operations are efficient to extract local features but easy to ignore global information, while self-attention mechanisms are expert in capturing long-distance dependencies but difficult to pay attention to local feature details. To discover comprehensive features for a given sequence as far as possible, we propose a Dual-branch model combining Self-Attention and Convolution, dubbed as DSAC, which fuses local features and global representations in an interactive way. In terms of features, convolution and self-attention contribute to feature extraction collaboratively, enhancing the representation learning. In terms of structure, a lightweight but efficient architecture of network is designed for the prediction, in particular, the dual-branch structure makes the convolution and the self-attention mechanism can be fully utilized to improve the predictive ability of our model. The experiment results on 165 ChIP-seq datasets show that DSAC obviously outperforms other five deep learning based methods and demonstrate that our model can effectively predict TFBSs based on sequence feature alone. The source code of DSAC is available at https://github.com/YuBinLab-QUST/DSAC/.

As a prerequisite step of scene text reading, scene text detection is known as a challenging task due to natural scene text diversity and variability. Most existing methods either adopt bottom-up sub-text component extraction or focus on top-down text contour regression. From a hybrid perspective, we explore hierarchical text instance-level and component-level representation for arbitrarily-shaped scene text detection. In this work, we propose a novel Hierarchical Graph Reasoning Network (HGR-Net), which consists of a Text Feature Extraction Network (TFEN) and a Text Relation Learner Network (TRLN). TFEN adaptively learns multi-grained text candidates based on shared convolutional feature maps, including instance-level text contours and component-level quadrangles. In TRLN, an inter-text graph is constructed to explore global contextual information with position-awareness between text instances, and an intra-text graph is designed to estimate geometric attributes for establishing component-level linkages. Next, we bridge the cross-feed interaction between instance-level and component-level, and it further achieves hierarchical relational reasoning by learning complementary graph embeddings across levels. Experiments conducted on three publicly available benchmarks SCUT-CTW1500, Total-Text, and ICDAR15 have demonstrated that HGR-Net achieves state-of-the-art performance on arbitrary orientation and arbitrary shape scene text detection.

Document object detection is a challenging task due to layout complexity and object diversity. Most of existing methods mainly focus on vision information, neglecting representative inherent spatial-related relationship among document objects. To capture structural information and contextual dependencies, we propose a novel document object detector based on spatial-related relation and vision (SRRV). It consists of three parts: vision feature extraction network, relation feature aggregation network and result refinement network. Vision feature extraction network enhances information propagation of hierarchical feature pyramid by adopting feature augmentation paths. Then, relation feature aggregation network combines graph construction module and graph learning module. Specifically, graph construction module calculates spatial information from geometric attributes of region proposals to encode relation information, while graph learning module stacks Graph Convolutional Network (GCN) layers to aggregate relation information at global scale. Both the vision and relation features are fed into result refinement network for feature fusion and relational reasoning. Experiments on the PubLayNet, POD and Article Regions datasets demonstrate that spatial relation information improves the performance with better accuracy and more precise bounding box prediction.

In this research, we investigate the possibility of applying ranking task activity in teaching and learning software engineering courses. We introduce three types of ranking tasks, conceptual-, contextual- and sequential ranking questions, which cover most core topics such as requirement analysis, architecture design and quality validation in the course. We have also done experiments on a group of students to see if ranking tasks could increase their conceptual knowledge in specific areas. Assessments were given in order to evaluate the effectiveness of this activity, showing an obvious increase in complex conceptual understanding.

Teaching design patterns in computer science introductory sequence is more difficult than learning it. We present a Just-in-Time-Teaching (JiTT) practice in Software Design Patterns course targeted to entry level students. We proposed a model that combines JiTT concepts with didactic considerations, preparation materials, in-class sessions and post-lecture assignments. We have effectively used this approach to teach two undergraduate classes. The paper also presents the results of a survey conducted to identify the effectiveness of the JiTT activities through warm-up questions, class-wide discussions, experiments and exercises.

Learning centered teaching becomes an important factor in a global perspective of learning software engineering. The Just-in-Time Teaching approach is used in a Chinese-German empirical case study. In a one year terminated project we will analyze the performance of our students in an active learning scenario with Just-in-Time Teaching and Peer Instruction. We will contribute an inter-cultural comparison of achieved competencies by student's self-assessment and teacher's observation.