LLC hardware prefetchers are a pivotal technique in modern high-performance processors for hiding memory access long latency. However, even accurate prefetch requests generated by LLC prefetchers experience long latency when they must traverse the LLC before accessing off-chip main memory.

Accelerating load requests is an effective way to improve processor performance through reducing load request latency. Currently, state-of-the-art methods, including Hermes and TLP, are limited to accelerating off-chip load requests that are correctly predicted and cannot accelerate mispredicted off-chip load requests and any on-chip load requests. To overcome this limitation, we propose a new technique called Apollo that integrates TLP. The key innovation of Apollo lies in its transformation of the perceptron-based off-chip prediction paradigm, pioneered by Hermes, into a comprehensive multi-level cache miss prediction technique. The workflow of Apollo is as follows: (1) predicting whether a load request will miss the L1D or L2, and (2) performing arbitration to decide whether to issue a speculative load request and to which cache level the request is issued, and (3) issuing a speculative load request to the lower-level cache (either L2 or LLC) after arbitration for those predicted to miss the L1D or L2, while allowing the regular load request to concurrently access the cache hierarchy. If the prediction is correct, the regular load request eventually misses the L1D or L2 and waits for the speculative load request to finish. Therefore, Apollo can hide the L1D access latency for correctly predicted L1D miss load requests, and both L1D and L2 access latency for correctly predicted L2 miss load requests. To enable Apollo, we propose a lightweight L1D miss load predictor (L1MP), a lightweight L2 miss load predictor (L2MP), and an arbiter called Athena. L1MP and L2MP predict whether load requests will miss in the L1D and L2, respectively, while Athena performs arbitration to control the issuance of speculative load requests. Our evaluation using a diverse set of workloads shows that Apollo provides a geometric mean (geomean) performance improvement of 15.2% for the single-core processor, outperforming Hermes by 9.6% and TLP by 6.4%. For the multi-core processor, Apollo provides a geomean performance improvement of 18.5%, which is 16.8% higher than Hermes and 5.8% higher than TLP.

Current electronic commerce (e-commerce) customer service systems often rely on retrieval-based methods, resulting in impersonal and emotionally flat responses. These systems struggle to handle diverse expressions and emotional nuances in user queries, leading to poor user experience . In this paper, we applied artificial intelligence techniques , notably the Gated Recurrent Unit model, to generate emotionally resonant dialogues in e-commerce customer service systems. We integrate intent recognition, emotion recognition, and topic expansion for accurate intent identification and nuanced responses. The model is trained on a customer service dataset from Jing Dong, a leading Chinese e-commerce platform. Through extensive experiments, we conducted comparative analyses against benchmark model Gated Recurrent Unit neural network . The results demonstrate that our model significantly outperforms existing approaches, achieving fluency, rationality, and emotionality scores of 62%, 58% and 61% respectively. These findings indicate that our approach generates responses with greater consistency to user intent and richer emotional content. The significance of these results lies in their potential to enhance user experience in e-commerce customer service interactions by providing more personalized and emotionally resonant automated responses.

This study investigates factors that affect customer satisfaction and the degree that service with an e-smile, service quality and customer mood affect consumer satisfaction. Data from 366 valid samples were obtained using an online survey. The research model is assessed using partial least squares analysis. The results show that customer satisfaction is predicted collectively by e-smile service, service quality and customer mood. Customer satisfaction with online service is predicted primarily by service quality, followed by customer mood and service with an e-smile. The results suggest that service with an e-smile provides considerable explanatory power for service quality and customer mood.

Speech plays an important part in human-computer interaction. As a major branch of speech processing, speech emotion recognition (SER) has drawn much attention of researchers. Excellent discriminant features are of great importance in SER. However, emotion-specific features are commonly mixed with some other features. In this paper, we introduce an approach to pull apart these two parts of features as much as possible. First we employ an unsupervised feature learning framework to achieve some rough features. Then these rough features are further fed into a semi-supervised feature learning framework. In this phase, efforts are made to disentangle the emotion-specific features and some other features by using a novel loss function, which combines reconstruction penalty, orthogonal penalty, discriminative penalty and verification penalty. Orthogonal penalty is utilized to disentangle emotion-specific features and other features. The discriminative penalty enlarges inter-emotion variations, while the verification penalty reduces the intra-emotion variations. Evaluations on the FAU Aibo emotion database show that our approach can improve the speech emotion classification performance.

Emotion-based features are critical for achieving high performance in a speech emotion recognition (SER) system. In general, it is difficult to develop these features due to the ambiguity of the ground-truth. In this paper, we apply several unsupervised feature learning algorithms (including K -means clustering, the sparse auto-encoder, and sparse restricted Boltzmann machines), which have promise for learning task-related features by using unlabeled data, to speech emotion recognition. We then evaluate the performance of the proposed approach and present a detailed analysis of the effect of two important factors in the model setup, the content window size and the number of hidden layer nodes. Experimental results show that larger content windows and more hidden nodes contribute to higher performance. We also show that the two-layer network cannot explicitly improve performance compared to a single-layer network.

Deep learning systems, such as Convolutional Neural Networks (CNNs), can infer a hierarchical representation of input data that facilitates categorization. In this paper, we propose to learn affect-salient features for Speech Emotion Recognition (SER) using semi-CNN. The training of semi-CNN has two stages. In the first stage, unlabeled samples are used to learn candidate features by contractive convolutional neural network with reconstruction penalization. The candidate features, in the second step, are used as the input to semi-CNN to learn affect-salient, discriminative features using a novel objective function that encourages the feature saliency, orthogonality and discrimination. Our experiment results on benchmark datasets show that our approach leads to stable and robust recognition performance in complex scenes (e.g., with speaker and environment distortion), and outperforms several well-established SER features.

As an essential way of human emotional behavior understanding, speech emotion recognition (SER) has attracted a great deal of attention in human-centered signal processing. Accuracy in SER heavily depends on finding good affect- related , discriminative features. In this paper, we propose to learn affect-salient features for SER using convolutional neural networks (CNN). The training of CNN involves two stages. In the first stage, unlabeled samples are used to learn local invariant features (LIF) using a variant of sparse auto-encoder (SAE) with reconstruction penalization. In the second step, LIF is used as the input to a feature extractor, salient discriminative feature analysis (SDFA), to learn affect-salient, discriminative features using a novel objective function that encourages feature saliency, orthogonality, and discrimination for SER. Our experimental results on benchmark datasets show that our approach leads to stable and robust recognition performance in complex scenes (e.g., with speaker and language variation, and environment distortion) and outperforms several well-established SER features.

Functional paralanguage includes considerable emotion information, and it is insensitive to speaker changes. To improve the emotion recognition accuracy under the condition of speaker-independence, a fusion method combining the functional paralanguage features with the accompanying paralanguage features is proposed for the speaker-independent speech emotion recognition. Using this method, the functional paralanguages, such as laughter, cry, and sigh, are used to assist speech emotion recognition. The contributions of our work are threefold. First, one emotional speech database including six kinds of functional paralanguage and six typical emotions were recorded by our research group. Second, the functional paralanguage is put forward to recognize the speech emotions combined with the accompanying paralanguage features. Third, a fusion algorithm based on confidences and probabilities is proposed to combine the functional paralanguage features with the accompanying paralanguage features for speech emotion recognition. We evaluate the usefulness of the functional paralanguage features and the fusion algorithm in terms of precision, recall, and F1-measurement on the emotional speech database recorded by our research group. The overall recognition accuracy achieved for six emotions is over 67% in the speaker-independent condition using the functional paralanguage features.