Reviewerly/exHarmony huggingface dataset:
A Convolutional Attention Network for Unifying General and Sequential Recommenders

General recommenders and sequential recommenders are two modeling paradigms of recommender. The main focus of a general recommender is to identify long-term user preferences, while the user’s sequential behaviors are ignored and sequential recommenders try to capture short-term user preferences by exploring item-to-item relations, failing to consider general user preferences. Recently, better performance improvement is reported by combining these two types of recommenders. However, most of the previous works typically treat each item separately and assume that each user–item interaction in a sequence is independent. This may be a too simplistic assumption, since there may be a particular purpose behind buying the successive item in a sequence. In fact, a user makes a decision through two sequential processes, i.e., start shopping with a particular intention and then select a specific item which satisfies her/his preferences under this intention. Moreover, different users usually have different purposes and preferences, and the same user may have various intentions. Thus, different users may click on the same items with an attention on a different purpose. Therefore, a user’s behavior pattern is not completely exploited in most of the current methods and they neglect the distinction between users’ purposes and their preferences. To alleviate those problems, we propose a novel method named, CAN, which takes both users’ purposes and preferences into account for the next-item recommendation. We propose to use Purpose-Specific Attention Unit (PSAU) in order to discriminately learn the representations of user purpose and preference. The experimental results on real-world datasets demonstrate the advantages of our approach over the state-of-the-art methods. • A unified framework to model the users’ purposes and personal preferences. • Purpose specific Attention Unit to attend important items according to user purposes and preferences. • Use PSAU in both long- and short-term interacted itemset to generate high-level hybrid user representation. • Conduct extensive experiments on two real-world datasets.
Optimal design and economic feasibility of rooftop photovoltaic energy system for Assuit University, Egypt

Rooftop Photovoltaic (RTPV) systems have gained more interest due to modularity and environmental friendliness. This article proposes an RTPV system for fulfilling the load demand of the main campus of Assuit University. The proposed system's economic and technical feasibility is comprehensively explored, including the expense and reliability. The system's sizing is implemented as a constrained optimization challenge. Particle Swarm Optimization (PSO) is employed to identify the proposed RTPV modules' optimal quantity, considering expanding Assuit University until 2025. Robust and reasonably accurate load forecasting models are developed and examined, including medium and long terms, to identify the monthly/annual load peaks from 2019 to 2025. The sizing procedure's PSO outcomes are validated via their comparison with Software such as PVsyst and PVGIS. The results indicated the economic efficacy of the proposed RTPV system and the ability of PSO to yield improved sizing than the other Software because of the well-formed objective function.
Interactive Digital Fabrication Machine Control Directly Within a CAD Environment

Interactive fabrication aims to close the gap between design and fabrication, allowing for rich interactions with materials and reflection in action. Drawing from craft practice, we contribute software that enables real-time control of digital fabrication machines from a Computer-Aided Design (CAD) environment. Our software not only allows interactive control of toolpath geometry, but also enables the control of machine parameters such as speed, acceleration, or jerk. This creates new opportunities for toolpath and material exploration. We evaluate our software with a professional glass artist on a custom digital fabrication machine that can accommodate multiple tools such as brushes, engraving bits, or microscopes. Finally, we reflect on implications for machine control.
Multi-modal program inference: a marriage of pre-trained language models and component-based synthesis

Multi-modal program synthesis refers to the task of synthesizing programs (code) from their specification given in different forms, such as a combination of natural language and examples. Examples provide a precise but incomplete specification, and natural language provides an ambiguous but more "complete" task description. Machine-learned pre-trained models (PTMs) are adept at handling ambiguous natural language, but struggle with generating syntactically and semantically precise code. Program synthesis techniques can generate correct code, often even from incomplete but precise specifications, such as examples, but they are unable to work with the ambiguity of natural languages. We present an approach that combines PTMs with component-based synthesis (CBS): PTMs are used to generate candidates programs from the natural language description of the task, which are then used to guide the CBS procedure to find the program that matches the precise examples-based specification. We use our combination approach to instantiate multi-modal synthesis systems for two programming domains: the domain of regular expressions and the domain of CSS selectors. Our evaluation demonstrates the effectiveness of our domain-agnostic approach in comparison to a state-of-the-art specialized system, and the generality of our approach in providing multi-modal program synthesis from natural language and examples in different programming domains.
Coherent self-control of free-space optical beams with integrated silicon photonic meshes

In technologies operating at light wavelengths for wireless communication, sensor networks, positioning, and ranging, a dynamic coherent control and manipulation of light fields is an enabling element for properly generating and correctly receiving free-space optical (FSO) beams even in the presence of unpredictable objects and turbulence in the light path. In this work, we use a programmable mesh of Mach–Zehnder (MZI) interferometers to automatically control the complex field radiated and captured by an array of optical antennas. The implementation of local feedback control loops in each MZI stage, without global multivariable optimization techniques, enables an unlimited scalability. Several functionalities are demonstrated, including the generation of perfectly shaped beams with nonperfect optical antennas, the imaging of a desired field pattern through an obstacle or a diffusive medium, and the identification of an unknown obstacle inserted in the FSO path. Compared to conventional devices used for the manipulation of FSO beams, such as spatial light modulators, our programmable device can self-configure through automated control strategies and can be integrated with other functionalities implemented onto the same photonic chip.
Face detection algorithm based on improved TinyYOLOv3 and attention mechanism

An improved face detection method ground on TinyYOLOv3 algorithm is put forward in this paper in view of the low recognition rate of traditional face detection methods in complex background and the long detection time of existing face detection methods ground on deep learning The main network of TinyYOLOv3 is redesigned to extract more abundant semantic information, which is increasing the number of network layers. The deep separable convolution is used instead of the traditional convolution and the features of different network layers are fused. Meanwhile, the accuracy of face detection is guaranteed, size of this network model is reduced. The CIoU (complete intersection over union) loss is used on improving this original prediction loss of bounding box coordinates. The channel attention mechanism is integrated into the feature extraction network to improve the positioning accuracy and detection accuracy. The network convergence speed is accelerated by hyperparameter optimization and priori box clustering method. The face detection experiments were conducted on the Wider Face data sets. And the experimental results manifest that the proposed algorithm has high recognition accuracy in complex scenes. At the same time, the proposed algorithm is better than others in terms of detection speed and model size, and can meet the requirements of embedded devices.
Exploring Data Analytics Without Decompression on Embedded GPU Systems

With the development of computer architecture, even for embedded systems, GPU devices can be integrated, providing outstanding performance and energy efficiency to meet the requirements of different industries, applications, and deployment environments. Data analytics is an important application scenario for embedded systems. Unfortunately, due to the limitation of the capacity of the embedded device, the scale of problems handled by the embedded system is limited. In this paper, we propose a novel data analytics method, called G-TADOC, for efficient text analytics directly on compression on embedded GPU systems. A large amount of data can be compressed and stored in embedded systems, and can be processed directly in the compressed state, which greatly enhances the processing capabilities of the systems. Particularly, G-TADOC has three innovations. First, a novel fine-grained thread-level workload scheduling strategy for GPU threads has been developed, which partitions heavily-dependent loads adaptively in a fine-grained manner. Second, a GPU thread-safe memory pool has been developed to handle inconsistency with low synchronization overheads. Third, a sequence-support strategy is provided to maintain high GPU parallelism while ensuring sequence information for lossless compression. Moreover, G-TADOC involves special optimizations for embedded GPUs, such as utilizing the CPU-GPU shared unified memory. Experiments show that G-TADOC provides 13.2× average speedup compared to the state-of-the-art TADOC. G-TADOC also improves performance-per-cost by 2.6× and energy efficiency by 32.5× over TADOC.
Deconfounded Image Captioning: A Causal Retrospect

Dataset bias in vision-language tasks is becoming one of the main problems which hinders the progress of our community. Existing solutions lack a principled analysis about why modern image captioners easily collapse into dataset bias. In this paper, we present a novel perspective: Deconfounded Image Captioning (DIC), to find out the answer of this question, then retrospect modern neural image captioners, and finally propose a DIC framework: DICv1.0 to alleviate the negative effects brought by dataset bias. DIC is based on causal inference, whose two principles: the backdoor and front-door adjustments, help us review previous studies and design new effective models. In particular, we showcase that DICv1.0 can strengthen two prevailing captioning models and can achieve a single-model 131.1 CIDEr-D and 128.4 c40 CIDEr-D on Karpathy split and online split of the challenging MS COCO dataset, respectively. Interestingly, DICv1.0 is a natural derivation from our causal retrospect, which opens promising directions for image captioning.
Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification

Recent developments in Quantum Computing (QC) have paved the way for an enhancement of computing capabilities. Quantum Machine Learning (QML) aims at developing Machine Learning (ML) models specifically designed for quantum computers. The availability of the first quantum processors enabled further research, in particular the exploration of possible practical applications of QML algorithms. In this work, quantum formulations of the Support Vector Machine (SVM) are presented. Then, their implementation using existing quantum technologies is discussed and Remote Sensing (RS) image classification is considered for evaluation.
A Deep Insight into Measuring Face Image Utility with General and Face-specific Image Quality Metrics

Quality scores provide a measure to evaluate the utility of biometric samples for biometric recognition. Biometric recognition systems require high-quality samples to achieve optimal performance. This paper focuses on face images and the measurement of face image utility with general and face-specific image quality metrics. While face-specific metrics rely on features of aligned face images, general image quality metrics can be used on the global image and relate to human perceptions. In this paper, we analyze the gap between the general image quality metrics and the face image quality metrics. Our contribution lies in a thorough examination of how different the image quality assessment algorithms relate to the utility for the face recognition task. The results of image quality assessment algorithms are further compared with those of dedicated face image quality assessment algorithms. In total, 25 different quality metrics are evaluated on three face image databases, BioSecure, LFW, and VGGFace2 using three open-source face recognition solutions, SphereFace, ArcFace, and FaceNet. Our results reveal a clear correlation between learned image metrics to face image utility even without being specifically trained as a face utility measure. Individual handcrafted features lack general stability and perform significantly worse than general face-specific quality metrics. We additionally provide a visual insight into the image areas contributing to the quality score of a selected set of quality assessment methods.
Source Data-free Unsupervised Domain Adaptation for Semantic Segmentation

Deep\footnote learning-based semantic segmentation methods require a huge amount of training images with pixel-level annotations. Unsupervised domain adaptation (UDA) for semantic segmentation enables transferring knowledge learned from the synthetic data (source domain) with low-cost annotations to the real images (target domain). However, current UDA methods mostly require full access to the source domain data for feasible adaptation, which limits their applications in real-world scenarios with privacy, storage, or transmission issues. To this end, this paper identifies and addresses a more practical but challenging problem of UDA for semantic segmentation, where access to the original source domain data is forbidden. In other words, only the pre-trained source model and unlabelled target domain data are available for adaptation. To tackle the problem, we propose to construct a set of source domain virtual data to mimic the source domain distribution by identifying the target domain high-confidence samples predicted by the pre-trained source model. Then by analyzing the data properties in the cross-domain semantic segmentation tasks, we propose an uncertainty and prior distribution-aware domain adaptation method to align the virtual source domain and the target domain with both adversarial learning and self-training strategies. Extensive experiments on three cross-domain semantic segmentation datasets with in-depth analyses verify the effectiveness of the proposed method.
FixyFPGA: Efficient FPGA Accelerator for Deep Neural Networks with High Element-Wise Sparsity and without External Memory Access

Convolutional neural networks (CNNs) have become very popular in real-time computer vision systems. CNNs involve a large amount of computation and storage and typically demand a highly efficient computing platform. Researchers have explored a diverse range of software and hardware optimizations to accelerate CNN inference in recent years. The high power consumption of GPUs and the lack of flexibility with ASIC has promoted interest in FPGAs as a promising platform to efficiently accelerate these CNN inference tasks. Various FPGA-based CNN accelerators have been proposed to low precision weights and high-sparsity in various forms. However, most of the previous work requires off-chip DDR memory to store the parameters and expensive DSP blocks to perform the computation. In this work, we propose the FixyFPGA, a fully on-chip CNN inference accelerator that naturally supports high-sparsity and low-precision computation. In our design, the weights of the trained CNN network are hard-coded into hardware and used as fixed operand for the multiplication. Convolution is performed by streaming the input images to the compute engine in a fully-paralleled, fully-pipelined manner. We analyzed the performance of the proposed scheme with both image classification tasks and object detection tasks based on the low precision, sparse compact CNN models. Compared to prior works, our design achieved 2.34× higher GOPS on ImageNet classification and 3.82× higher frames per second on Pascal VOC object detection.
Show, Read and Reason

We investigate the challenging problem of table structure recognition in this work. Many recent methods adopt graph-based context aggregator with strong inductive bias to reason sparse contextual relationships of table elements. However, the strong constraints may be too restrictive to represent the complicated table relationships. In order to learn more appropriate inductive bias from data, we try to introduce Transformer as context aggregator in this work. Nevertheless, Transformer taking dense context as input requires larger scale data and may suffer from unstable training procedure due to the weakening of inductive bias. To overcome the above limitations, we in this paper design a FLAG (FLexible context AGgregator), which marries Transformer with graph-based context aggregator in an adaptive way. Based on FLAG, an end-to-end framework requiring no extra meta-data or OCR information, termed FLAG-Net, is proposed to flexibly modulate the aggregation of dense context and sparse one for the relational reasoning of table elements. We investigate the modulation pattern in FLAG and show what contextual information is focused, which is vital for recognizing table structure. Extensive experimental results on benchmarks demonstrate the performance of our proposed FLAG-Net surpasses other compared methods by a large margin.
A reversible data hiding scheme for encrypted images with pixel difference encoding

In recent years, reversible data hiding of encrypted images (RDHEI) has attracted extensive attention in academia. Pixel difference is an important technique for embedding hiding information. In this paper, we propose a reversible data hiding scheme for encrypted images based on pixel prediction and pixel difference. To be specific, we first use the MED predictor to generate the predicted image, and then subtract the original image from the predicted image to obtain the difference image. Then, the histogram shifting method is used to preprocess the difference image and retain the overflow pixel value. Then, block scrambling and pixel scrambling are used to encrypt the preprocessed image. Finally, the encrypted image is embedded with pixel difference technology. The proposed scheme makes full use of the correlation of pixels and has high utilization of images. In addition, we propose a cover image selection mechanism. Experimental results show that our scheme can achieve a higher embedding rate than some of the state-of-the-art RDHEI schemes.
MC-LCR: Multimodal contrastive classification by locally correlated representations for effective face forgery detection

As the remarkable development of facial manipulation technologies is accompanied by severe security concerns, face forgery detection has spurred recent research. Most detection methods train a binary classifier under global supervision to judge whether a face is real or fake. However, advanced manipulations only perform small-scale tampering, posing challenges to comprehensively capturing subtle and local forgery artifacts, especially in high-compression settings and cross-dataset scenarios. To address such limitations, we propose a framework, multimodal contrastive classification by locally correlated representations (MC-LCR), for effective face forgery detection. Instead of specific appearance features, MC-LCR amplifies implicit local discrepancies between authentic and forged faces from both the spatial and frequency domains. A shallow style representation block measures the pairwise correlation of shallow feature maps, encoding local style information to extract more discriminative features in the spatial domain. We observe that subtle forgery artifacts can be further exposed in the patch-wise phase and amplitude spectrum, and that they exhibit different clues. According to the complementarity of amplitude and phase information, we develop a patch-wise amplitude and phase dual attention module to capture locally correlated inconsistencies in the frequency domain. The collaboration of supervised contrastive loss with cross-entropy loss helps the network learn more discriminative and generalized representations. Through extensive experiments and comprehensive studies, we achieve state-of-the-art performance and demonstrate the robustness and generalization of our method.
Attention Based Semantic Segmentation on UAV Dataset for Natural Disaster Damage Assessment

The detrimental impacts of climate change include stronger and more destructive hurricanes happening all over the world. Identifying different damaged structures of an area including buildings and roads are vital since it helps the rescue team to plan their efforts to minimize the damage caused by a natural disaster. Semantic segmentation helps to identify different parts of an image. We implement a novel self-attention based semantic segmentation model on a high resolution UAV dataset and attain Mean IoU score of around 88% on the test set. The result inspires to use self-attention schemes in natural disaster damage assessment which will save human lives and reduce economic losses.
I-GCN: Incremental Graph Convolution Network for Conversation Emotion Detection

Sentiment analysis and emotion detection in conversation are becoming hot topics in regard to several applications. With the development of the social robot, social network, and intelligent voice assistant, emotion detection is attracting more attention as a key component in these research fields. Many approaches have been proposed to handle this problem in recent years. However, these previous approaches focus on either the temporal change information of the conversation or the semantic correlation information of the dialogue but ignore the combination of temporal information and semantic correlation information. In this paper, we propose an incremental graph convolution network (I-GCN) to handle emotion detection in conversation. We first utilize the graph structure to represent conversation at different times, which can represent the semantic correlation information of utterances. Then, we apply the incremental graph structure to imitate the process of dynamic conversation, which can preserve the temporal change information of conversation. Especially, for the first step of the process, we creatively propose utterance-level GCN (U-GCN) and speaker-level GCN (S-GCN) to learn the features of utterances for emotion detection. U-GCN focuses on the correlations among utterances and applies the multi-head attention model to find latent correlation information among utterances, which aims to further enhance the guidance of semantic relevance for feature learning. S-GCN focuses on the correlation between speaker and utterances, which can provide a different angle to guide the feature learning of utterances. In the learning of model parameters, we constantly utilize the new utterances to fine-tune the parameters of GNN for enhancement of the contribution of temporal change information. Detailed evaluations of the proposed method on three published conversation corpuses demonstrate the great effectiveness of our approach over several conventional competitive baselines.