Reviewerly/exHarmony huggingface dataset:
Speech recognition using Taylor-gradient Descent political optimization based Deep residual network

A Speech is an effective and most favoured communication mode amongst the human. It is normal for people to anticipate speech interfaces with computer. For real-time intelligent applications, it is necessary that the machine can hear, understand, investigate, and take action upon receiving the input information from speaker. This can be taken by introducing an Automatic Speech Recognition system, which translates an audio signal into a written text or a command without understanding what has been recognized. Several methods are designed for speech recognition, but accuracy is a most challenging task. Hence, this paper develops a novel Taylor Gradient Descent Political Optimizer (Taylor GDPO) based deep learning model for speech recognition. A developed Taylor GDPO is obtained by integrating Taylor series, Gradient Descent (GD) and Political Optimizer (PO). Firstly, pre-processing of an input signal is done and the features are extracted. Then, the extracted features are given as input to the Deep Residual Network (DRN), which is trained by the developed Taylor GDPO. A developed model provided effectual efficiency with the highest accuracy of 96.93%, smallest False Acceptance Rate (FAR) of 2.44%, smallest False Rejection Rate (FRR) of 2.10%, smallest Mean squared error (MSE) of 0.038.
Novel Modeling of Efficient Data Deduplication for Effective Redundancy Management in Cloud Environment

A lightweight and robust security mechanism for data deduplicated data is one of the essential requirements from both security and storage optimization viewpoints. However, existing schemes for securing deduplication on the cloud have largely focused on incorporating sophisticated encryption technique, with relatively little focus on facilitating computationally efficient deduplication. Hence, a wide trade-off is being explored between robust security demands and efficient redundancy management of data over cloud storage. This problem is addressed in the proposed study, where a simplified solution is presented, emphasizing the over deduplication principle, which will complement the supporting usage of the further security scheme. According to proposed scheme, the data is subjected to artifact removal operation followed by manual labeling and a unique clustering mechanism to group deduplicated data. The experimental analysis showcases the proposed scheme to excel better performance in contrast to the existing system concerning computational resource efficiency.
Correlation Filter Tracker With Sample-Reliability Awareness and Self-Guided Update

In visual tracking, unreliable samples always exist because of occlusion, illumination variation, motion blur, etc. Existing studies have effectively improved the performance of trackers by enhancing the quality of online samples. However, an underappreciated view is that not all samples are equally essential to model training. In this paper, we propose a Sample-Aware Adaptive Updating (SAAU) strategy which can actively adjust the update formula by sensing the reliability of samples. Specifically, the Sample-Reliability Awareness (SRA) module can quantify sample reliability by calculating three specific indicators, where the Residual Peak-to-Correlation Energy (RPCE) is designed to cooperate with the other two introduced indicators to obtain credit scores on each sample. Besides, the Self-Guided Update (SGU) module provides a tracker with an unfixed learning rate that matches with the reliability label during updating, where our label annotator generates the label. Extensive experiments on several public benchmarks demonstrate the outstanding compatibility of SAAU and the superiority of our tracker (SAAU-CF) over state-of-the-art approaches.
Easy Peasy: A New Handy Method for Pairing Multiple COTS IoT Devices

Context-based paring is a promising direction for pairing IoT devices constrained in user interfaces (UIs). However, it takes a proximate distance or a long time for IoT devices to sense highly correlated context with enough entropy. In this work, we present a fast and secure approach, named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MPairing</small> , to pairing multiple commercial off-the-shelf (COTS) IoT devices. This approach is based on the key idea that devices co-located within a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">physically-secure boundary</i> can perceive qualified context under the help of human-in-the-loop (HITL). Specifically, we leverage received-signal-strength (RSS) trajectory data with manually-generated interference in a short period as the shared secret to achieve fast and secure pairing. Subsequently, the real-time RSS trajectory data is utilized to generate random numbers in lieu of pre-shared key (PSK), which makes our scheme more resistant to background attacks. We theoretically prove the security of our pairing scheme and implement it in real-world environments. Our experimental results demonstrate that our scheme can effectively defend against malicious devices by imposing a threshold on the similarity of RSS trajectory data. The experimental results also show that, compared with the traditional context-based pairing that takes up to 24 hours, in our scheme it takes only 10 seconds on average for a legitimate device to pass the similarity checking, which is efficient and robust.
Optimized Sage–Husa filtering method for multitarget tracking with unknown detection profile

The Sage–Husa filter can estimate states of multitarget from available measurements. To address the imprecise estimations of the conventional Sage–Husa filter, an optimized method and its implementation are put forward in this study. Unlike previous approaches, the beta distribution for unknown detection profile is improved, and the transfer probability is applied to update the implicit probability of detection. With respect to the positive definiteness of the estimated covariance, the Cholesky decomposition is used in both prediction and update of the Sage–Husa filter. Recalling the estimated probability of detection, the track gate strategy in the proposed filtering framework is derived to distinguish clutter-generated and target-generated measurements. On the basis of updated measurements, both the state and the track of multitarget are achieved. Finally, the experiments of multitarget tracking are made to indicate the performance of the proposed Sage–Husa filtering method.
An Integrative Human-Centered Architecture for Interactive Programming Assistants

Programming has become a collaboration between human programmers, who drive intent, and interactive assistants that suggest contextually relevant editor actions. There has been considerable work on suggestion synthesis strategies&#x2014;from semantic autocomplete to modern program synthesis, repair, and machine learning research. This diversity of contextually viable strategies creates a need for an integrative, human-centered perspective on the problem of programming assistant design that (1) confronts the problem of integrating a variety of synthesis strategies, fed by shared semantic analyses capable of operating on program sketches, and (2) centers the needs of the human programmer: comprehending, comparing, ranking, and filtering suggestions generated by various synthesizers, and in some cases participating in a synthesizer&#x2019;s search by supplying additional expressions of intent. This paper contributes a conceptual architecture and API to guide programming assistant designers as they confront these integration and human-centered design challenges. We then instantiate this architecture with two prototype end-to-end assistant designs, both developed for the Hazel programming environment, that emphasize understudied design aspects, namely continuity, explainability, human-in-the-loop synthesis, and the integration of multiple analyses with multiple synthesis strategies.
Analyzing a Chess Engine Based on Alpha–Beta Pruning, Enhanced with Iterative Deepening

Chess is a two-player strategy board game played on a chessboard, a checkered game board with 64 squares arranged in an 8 × 8 grid. The current technological advancements have transformed the way we not only play chess but study chess techniques and analyze the beautiful game. Predicting the next move in a chess game is not an easy task, as the branching factor of Chess on average is 35. Thus, new algorithms need to be researched to make an ideal Game Engine. In this paper, we analyze why the traditional algorithms fail to work for a Chess Engine and create one such engine which evaluates the Game Tree using the traditional Alpha–Beta Algorithm, enhanced by using Iterative Deepening Search.
MFHE: Multi-View Fusion-Based Heterogeneous Information Network Embedding

Depending on the type of information network, information network embedding is classified into homogeneous information network embedding and heterogeneous information network (HIN) embedding. Compared with the homogeneous network, HIN composition is more complex and contains richer semantics. At present, the research on homogeneous information network embedding is relatively mature. However, if the homogeneous information network model is directly applied to HIN, it will cause incomplete information extraction. It is necessary to build a specialized embedding model for HIN. Learning information network embedding based on the meta-path is an effective approach to extracting semantic information. Nevertheless, extracting HIN embedding only from a single view will cause information loss. To solve these problems, we propose a multi-view fusion-based HIN embedding model, called MFHE. MFHE includes four parts: node feature space transformation, subview information extraction, multi-view information fusion, and training. MFHE divides HIN into different subviews based on meta-paths, models the local information accurately in the subviews based on the multi-head attention mechanism, and then fuses subview information through a spatial matrix. In this paper, we consider the relationship between subviews; thus, the MFHE is applicable to complex HIN embedding. Experiments are conducted on ACM and DBLP datasets. Compared with baselines, the experimental results demonstrate that the effectiveness of MFHE and HIN embedding has been improved.
A technique to improve text editing on smartphones

We present a technique that aims to make text editing on smartphones more efficient. One of the main difficulties of editing on smartphones is the cursor positioning since the characters are usually smaller than the user finger, and for this reason a number of editing techniques have been proposed. Our technique is primarily designed to help the user position the cursor in the desired location: after the user has tapped on the text to position the cursor, soft keys with directional arrows appear on the screen and allow the user to move the cursor by tapping on them. Moreover, in addition to cursor placement, the technique also allows to perform the main editing operations through additional dedicated buttons for select/cut/copy/paste.
BRFP: An Efficient and Universal Sentence Embedding Learning Model Method Supporting Fused Syntax Combined with Graph Embedding Representation Algorithm

Due to the rapidly growing volume of data on the Internet, the methods of efficiently and accurately processing massive text information have been the focus of research. In natural language processing theory, sentence embedding representation is an important method. This paper proposes a new sentence embedding learning model called BRFP (Factorization Process with Bidirectional Restraints) that fuses syntactic information, uses matrix decomposition to learn syntactic information, and fuses and calculates with word vectors to obtain the embedded representation of sentences. In the experimental chapter, text similarity experiments are conducted to verify the rationality and effectiveness of the model and analyzed experimental results on Chinese and English texts with the current mainstream learning methods, and potential improvement directions are summarized. The experimental results on Chinese and English datasets, including STS, AFQMC, and LCQMC, show that the model proposed in this paper outperforms the CNN method in terms of accuracy and F1 value by 7.6% and 4.8. The comparison experiment with the word vector weighted model shows that when the sentence length is longer, or the corresponding syntactic structure is complex, the model’s advantages in this paper are more prominent than TF-IDF and SIF methods. Compared with the TF-IDF method, the effect improved by 14.4%. Compared with the SIF method, it has a maximum advantage of 7.9%, and the overall improvement in each comparative experimental task is between 4 and 6 percentage points. In the neural network model comparison experiment, the model in this paper compared the CNN, RNN, LSTM, ST, QT, and InferSent models, and the effect significantly improved on the 14’OnWN, 14’Tweet-news, and 15’Ans.-forum datasets. For example, in the 14’OnWN dataset, the BRFP method has a 10.9% improvement over the ST method. The 14’Tweet-news dataset has a 22.9% advantage over the LSTM method, and the 15’Ans.-forum dataset has a 24.07% improvement over the RNN method. The article also demonstrates the generality of the model, proving that the model proposed in this paper is also a universal learning framework.
Robust Graph Regularized Nonnegative Matrix Factorization

Nonnegative Matrix Factorization (NMF) has become a popular technique for dimensionality reduction, and been widely used in machine learning, computer vision, and data mining. Existing unsupervised NMF methods impose the intrinsic geometric constraint on the encoding matrix, which only indirectly affects the base matrix. Moreover, they ignore the global structure of the data space. To address these issues, in this paper we propose a novel unsupervised NMF learning framework, called Robust Graph regularized Nonnegative Matrix Factorization (RGNMF). RGNMF constructs a sparse graph imposed on the basis matrix to catch the global structure and preserve the discriminative information. And it models the local structure by building a k-NN graph constrained on the encoding matrix, which gains the compact representation. Consequently, RGNMF not only respects the global structure, but also depicts the local structure. In addition, it employs such a L<sub>2,1</sub>-norm cost function to decompose the basis matrix and encoding matrix that its robustness can be improved. Further, it imposes the L<sub>2,1</sub>-norm constraint on the basis matrix to choose the discriminative feature. Hence, RGNMF can gain the robust discriminative representation by combining structure learning and L<sub>2,1</sub>-norm constraints imposed on the basis matrix and encoding matrix. Extensive experiments on real-world problems demonstrate that RGNMF achieves better clustering results than the state-of-the-art approaches.
Recaptured Screen Image Demoiréing in Raw Domain

Capturing screen content by smart-phone cameras has become a daily routine to record or share instant information from display screens for convenience. However, these recaptured screen images are often degraded by moiré patterns and usually present color cast against the original screen source. We observe that performing demoiréing in raw domain before feeding into the image signal processor (ISP) is more effective than demoiréing in the sRGB domain as done in recent demoiréing works. In this paper, we investigate the demoiréing of raw images through a class-specific learning approach. To this end, we build the first well-aligned raw moiré image dataset by pixel-wise alignment between the recaptured images and source ones. Noting that document images occupy a large portion of screen contents and have different properties from generic images, we propose a class-specific learning strategy for textual images and natural color images. In addition, to deal with moiré patterns with various scales, a multi-scale encoder with multi-level feature fusion is proposed. The shared encoder enables us to extract rich representations for the two kinds of contents and the class-specific decoders benefit the specific content reconstruction by focusing on targeted representations. Experiment results demonstrate that our method achieves state-of-the-art demoiréing performance. We have released the code and dataset in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tju-chengyijia/RDNet</uri>
H<sup>2</sup>-Stereo: High-Speed, High-Resolution Stereoscopic Video System

High-speed, high-resolution stereoscopic (H2-Stereo) video allows us to perceive dynamic 3D content at fine granularity. The acquisition of H2-Stereo video, however, remains challenging with commodity cameras. Existing spatial super-resolution or temporal frame interpolation methods provide compromised solutions that lack temporal or spatial details, respectively. To alleviate this problem, we propose a dual camera system, in which one camera captures high-spatial-resolution low-frame-rate (HSR-LFR) videos with rich spatial details, and the other captures low-spatial-resolution high-frame-rate (LSR-HFR) videos with smooth temporal details. We then devise a Learned Information Fusion network (LIFnet) that exploits the cross-camera redundancies to enhance both camera views to high spatiotemporal resolution (HSTR) for reconstructing the H2-Stereo video effectively. We utilize a disparity network to transfer spatiotemporal information across views even in large disparity scenes, based on which, we propose disparity-guided flow-based warping for LSR-HFR view and complementary warping for HSR-LFR view. A multi-scale fusion method in feature domain is proposed to minimize occlusion-induced warping ghosts and holes in HSR-LFR view. The LIFnet is trained in an end-to-end manner using our collected high-quality Stereo Video dataset from YouTube. Extensive experiments demonstrate that our model outperforms existing state-of-the-art methods for both views on synthetic data and camera-captured real data with large disparity. Ablation studies explore various aspects, including spatiotemporal resolution, camera baseline, camera desynchronization, long/short exposures and applications, of our system to fully understand its capability for potential applications.
Coherent effects of pulsed fast gate in 1D- and 2D ion quantum computer architectures

Trapped ions are one of the most promising platforms for quantum computing due to the longest qubit coherence times and the highest gate fidelities. However, scaling the number of ions (qubits) in a linear Coulomb crystal is the key difficulty on the way to multi-qubit systems. One of the promising pathways to scale the number of qubits is to implement the pulsed non-adiabatic gates based on the sequence of State Dependent Kicks (SDKs). We have analytically and numerically studied the influence of coherent effects in the SDK sequence and, correspondingly, have deduced the influence of the individual SDK error on the net gate's fidelity. We have shown that the coherence effects significantly impact the fidelity of non-adiabatic gates and must be taken into the account. As practical examples, we have developed a numerical model for full simulation of coherence effects using a linear ion microtrap array and a 2D microtrap array. We have also studied the dependency of the gate fidelity on the laser power fluctuations.
Graph Transformer Collaborative Filtering Method for Multi-Behavior Recommendations

Graph convolutional networks are widely used in recommendation tasks owing to their ability to learn user and item embeddings using collaborative signals from high-order neighborhoods. Most of the graph convolutional recommendation tasks in existing studies have specialized in modeling a single type of user–item interaction preference. Meanwhile, graph-convolution-network-based recommendation models are prone to over-smoothing problems when stacking increased numbers of layers. Therefore, in this study we propose a multi-behavior recommendation method based on graph transformer collaborative filtering. This method utilizes an unsupervised subgraph generation model that divides users with similar preferences and their interaction items into subgraphs. Furthermore, it fuses multi-headed attention layers with temporal coding strategies based on the user–item interaction graphs in the subgraphs such that the learned embeddings can reflect multiple user–item relationships and the potential for dynamic interactions. Finally, multi-behavior recommendation is performed by uniting multi-layer embedding representations. The experimental results on two real-world datasets show that the proposed method performs better than previously developed systems.
Anticipating User Intentions in Customer Care Dialogue Systems

In this article, we investigate the case of human-machine dialogues in the specific domain of commercial customer care. We built a corpus of conversations between users and a customer-care chatbot of an Italian Telecom Company, focusing on a sample of conversations where users contact the service asking for explanations about billing issues or overcharges. We observed that users’ requests are often vague, generic or incomprehensible. In such cases, commercial dialogue systems typically ask for clarifications or further details to fully understand users’ specific requests. However, from the corpus analysis it appeared that chatbot's clarifying requests may result in ineffective interactions, with users eventually giving up the conversation or switching to a human agent for a faster query resolution. A recovery strategy is thus needed to anticipate users’ information needs, or intentions. We address this issue resorting to GEN-DS, a dialogue system based on symbolic data-to-text generation. GEN-DS analyzes the user-company contextual relational knowledge, with the aim to generate more relevant answers to unclear questions. In this article, we describe the GEN-DS architecture along with the experiments we carried out to evaluate its output. Results from an offline human evaluation show significant improvements of GEN-DS compared to the original system. These improvements concern properties such as utility, necessity, understandability, and quickness of the information communicated in the dialogue. We believe that GEN-DS techniques may find application in all the dialogue systems that need to manage vague requests and must rely on relational knowledge.
Nessy: A Neuro-Symbolic System for Label Noise Reduction

Noisy labels represent one of the key issues in supervised machine learning. Existing work for label noise reduction mainly takes a probabilistic approach that infers true labels from data distributions in low-level feature spaces. Such an approach is not only limited by its capability to learn high-quality data representations, but also by the low predictive power of data distributions in inferring true classes. To address those problems, we introduce Nessy, a neuro-symbolic system that integrates deep probabilistic modeling and symbolic knowledge for label noise reduction. Our deep probabilistic model infers the true classes of data instances with noisy labels by exploiting data distributions in an underlying latent feature representation space. For data instances where inference is not reliable enough, Nessy extracts symbolic rules and ranks them according to several utility metrics. Top-ranking rules are injected into the deep probabilistic model via expectation regularization, i.e., via a posterior regularization term constraining the class distribution in the objective function. In a real deployment over multiple relation extraction tasks, we demonstrate that Nessy is able to significantly improve the state of the art, by 7&#x0025; accuracy and 10.7&#x0025; AUC on average.