Reviewerly/exHarmony huggingface dataset:
Solid-phase regeneration and electrochemical performance of waste lithium iron phosphate materials based on sensor and image technology

With the development of science and technology, the power source of the automobile industry has gradually changed from non-renewable energy sources such as petroleum to electric energy, a clean and renewable energy source. By the end of 2020, scientists have found that the global demand for batteries has greatly increased. The demand for power batteries has reached 50% of all battery demand, occupying half of the market. In addition, scientists also boldly predict that by 2025, China's consumption of batteries in the automobile industry will reach more than 100,000 tons per year. Faced with so much battery consumption, how to dispose of and recycle LiFePO4 used batteries has become the key to restricting the development of the industry. Failure to dispose of used batteries will not only hinder the development of the industry, but also cause serious pollution to the environment. The application of sensors today has been extended to various fields, and the emergence of satellite navigation and positioning technology is one of the driving forces to promote its development. However, the sensor cannot be directly applied to various environments. When in a complex environment, the obstruction of various objects in the environment will seriously affect the stability of the sensor. In order to ensure the normal operation of the sensor, positioning technology began to be applied to the sensor. The industrial production process of solid-phase regeneration and electrochemical performance of waste lithium iron phosphate materials discussed in this article is based on this perspective, through the use of computer vision positioning to reduce the impact of environmental factors on the accuracy of the sensor, thereby improving the quality of the battery. In order to match the image point of the waste lithium iron phosphate material with the space point, this paper creatively uses the image point and the point in the corresponding space field. In addition, the author also studied the camera's rear rendezvous method and proposed a new rear rendezvous method. In addition, in order to strengthen the detection of waste lithium iron phosphate materials, this paper strengthened the construction of the sensor hardware system and software system, and completed the upgrade of the waste lithium iron phosphate material detection system.
A conversational model for eliciting new chatting topics in open-domain conversation

In human conversations, the emergence of new topics is a key factor in enabling dialogues to last longer. Additional information brought by new topics can make the conversation more diverse and interesting. Chat-bots also need to be equipped with this ability to proactively elicit new chatting topics. However, previous studies have neglected the elicitation of new topics in open-domain conversations. At the same time, previous works have represented topics with word-level keywords or entities. However, a topic is open to multiple keywords and a keyword can reflect multiple potential topics. To move towards a fine-grained topic representation, we represent topic with topically related words. In this paper, we design a novel model, named CMTE, which focuses not only on coherence with context, but also brings up new chatting topics. In order to extract topic information from conversational utterances, a Topic Fetcher module is designed to fetch semantic-coherent topics with the help of topic model. To equip model with the ability to elicit new topics, a Topic Manager module is designed to associate the new topic with context. Finally, responses are generated by a well-designed fusion decoding mechanism to explicitly distinguish between topic words and general words. Experiment results show that our model is better than state of the art in automatic metrics and manual evaluations.
Training data selection for imbalanced cross-project defect prediction

• Cross-project defect prediction with effective training data is considered. • Jensen-Shannon divergence is applied to select the source project most similar to the target project. • A grouped synthetic minority oversampling technique is used to solve the imbalanced problem. • Results indicate that the proposed approach can improve the prediction performance. Machine learning methods have been applied in software engineering to effectively predict software defects. Researchers proposed cross-project defect prediction (CPDP) for cases in which few or no data are available. CPDP uses the labeled data of a source project to construct a prediction model for the target project. However, the prediction performance remains inferior because the training data selection for the source project is ineffective. In this paper, the Jensen-Shannon divergence is first applied to automatically select the source project most similar to the target project. Subsequently, a grouped synthetic minority oversampling technique (SMOTE) is applied to improve the class imbalance of the projects. Finally, relative density estimation is performed to select the data for the source project. The experimental results demonstrate that the proposed method improves the prediction performance and exhibits high adaptability to different classifiers.
Efficient Secret Image Sharing Scheme with Authentication and Cheating Prevention

Due to the widespread adoption and popularity of digital images in distributed storage, Secret Image Sharing (SIS) has attracted much attention. However, preventing the cheating of shares is an important problem that needs to be solved in the traditional SIS scheme. An adversary without image shares may participate in the restoration phase as a share owner. In this phase, the adversary can obtain real shares or prevent recovering real images by submitting fake shadows. Our schemes are based on the original Thien-Lin’s scheme. In the scheme I, we use some XOR operations to get two authentication codes through all secret pixel values to achieve a lightweight and fast-calculated authentication scheme for cheating prevention. This scheme is suitable for small devices with limited resources. In scheme II, we use a hash algorithm to generate the authentication code. This scheme is suitable for environments with larger storage space and higher security levels. Since all pixel values are involved in the authentication in our proposed schemes, it can prevent fake shadow images from cheating. Meanwhile, the shadow size is almost the same as the original Thien-Lin’s scheme. Experimental results and theoretical analysis show that the proposed schemes are feasible and effective.
An intelligent model to assess information systems security level

This research presents a model for assessing information systems cybersecurity maturity level. The main purpose of the model is to provide comprehensive support for information security specialists and auditors in checking information systems security level, checking security policy implementation, and compliance with security standards. The model synthesized based on controls and practices present in ISO 27001 and ISO 27002 and the neural network of direct signal propagation. The methodology described in this paper can also be extended to synthesis a model for different security control sets and, consequently, to verify compliance with another security standard or policy. The resulting model describes a real non-automated process of assessing the maturity of an IS at an acceptable level and it can be recommended to be used in the process of real audit of Information Security Management Systems.
Nonlinear Gaussian Mixture Filtering with Intrinsic Fault Resistance

Because of limitations in onboard computing, spaceflight navigation has long been relegated to estimation processes that promote computational efficiency over increased model accuracy. Such is the case with fault tolerance methods, where residual editing is the most common practice to screen out erroneous sensor data. This work investigates an alternative approach, where sensor returns classified as faulty are not simply rejected, but are accounted for within the measurement model of the sensor, ultimately leading to a filter with intrinsic fault resistance in lieu of supplementary extensions like residual editing. Several different faulty measurement models are examined by comparing the proposed filter to a baseline filter outfitted with residual editing, where analysis is performed to test relative performance, robustness, and approximation ability of the proposed filter. The proposed approach not only outperforms residual editing, but is also found to be exceptionally robust to both unknown and approximated faulty measurement distributions.
An international survey of applied face-matching training courses

Face matching decisions in applied settings are typically carried out by trained face-matching professionals, known as facial reviewers and facial examiners. Recent research has demonstrated that short professional face-matching training courses are limited in improving face-matching accuracy, however, despite the existence of high-level training guidelines produced by the practitioner community the content, duration and delivery of training for facial reviewers and facial examiners are not widely understood in the academic research community. The current study aimed to address this gap in the scientific literature to better understand how different agencies train facial reviewers and facial examiners, using results collected from an international survey. The survey included questions on the duration, delivery and content of an agencies face-matching training. 24 face-matching agencies from five continents took part in the survey. A quantitative analysis of the survey results revealed that at the group level facial examiners received longer durations of training, covering more topics than reviewers and more frequently included one-to-one mentoring. However, the survey revealed large differences in the duration, delivery methods and content of training by individual agencies at both the facial review and facial examiner level and low inclusion of evidence-based training practices. These results should help researchers to better understand the diversity in training practices and durations of training between different agencies and may help explain the individual differences observed in the performance of face matching professionals in the literature.
Knowledge-Based Reasoning Network For Object Detection

The mainstream object detection algorithms rely on recognizing object instances individually, but do not consider the high-level relationship among objects in context. This will inevitably lead to biased detection results, due to the lack of commonsense knowledge that humans often use to assist the task for object identification. In this paper, we present a novel reasoning module to endow the current detection systems with the power of commonsense knowledge. Specifically, we use graph attention network (GAT) to represent the knowledge among objects. The knowledge covers visual and semantic relations. Through the iterative update of GAT, the object features can be enriched. Experiments on the COCO detection benchmark indicate that our knowledge-based reasoning network has achieved consistent improvements upon various CNN detectors. We achieved 1.9 and 1.8 points higher Average Precision (AP) than Faster-RCNN and Mask-RCNN respectively, when using ResNet50-FPN as backbone.
Robust multimodal discrete hashing for cross-modal similarity search

Hashing technology improves the search efficiency and reduces the storage space of data. However, building an effective modal with unsupervised cross modal retrieval and generating efficient binary code is still a challenging task, considering of some issues needed to be further discussed and researched for unsupervised multimodal hashing. Most of the existing methods ignore the discrete restriction, and manually or experientially determine the weights of each modality. These limitations may significantly reduce the retrieval accuracy of unsupervised cross-modal hashing methods. To solve these problems, we propose a robust hash modal that can efficiently learn binary code by employing a flexible and noise-resistant ℓ2,1-loss with nonlinear kernel embedding. In addition, we introduce an intermediate state mapping that facilitate later modal optimization to measure the loss between the hash codes and the intermediate states. Experiments on several public multimedia retrieval datasets validate the superiority of the proposed method from various aspects.
Service humanoid robotics: a novel interactive system based on bionic-companionship framework

At present, industrial robotics focuses more on motion control and vision, whereas humanoid service robotics (HSRs) are increasingly being investigated and researched in the field of speech interaction. The problem and quality of human-robot interaction (HRI) has become a widely debated topic in academia. Especially when HSRs are applied in the hospitality industry, some researchers believe that the current HRI model is not well adapted to the complex social environment. HSRs generally lack the ability to accurately recognize human intentions and understand social scenarios. This study proposes a novel interactive framework suitable for HSRs. The proposed framework is grounded on the novel integration of Trevarthen’s (2001) companionship theory and neural image captioning (NIC) generation algorithm. By integrating image-to-natural interactivity generation and communicating with the environment to better interact with the stakeholder, thereby changing from interaction to a bionic-companionship. Compared to previous research a novel interactive system is developed based on the bionic-companionship framework. The humanoid service robot was integrated with the system to conduct preliminary tests. The results show that the interactive system based on the bionic-companionship framework can help the service humanoid robot to effectively respond to changes in the interactive environment, for example give different responses to the same character in different scenes.
Investigating Transparency Methods in a Robot Word-Learning System and Their Effects on Human Teaching Behaviors

Robots need to understand words for references in social spaces (e.g., objects, locations, actions). Grounded language learning systems aim to learn these words from observing a human tutor. Teaching a robot is difficult for naive users due to the discrepancy between the users' mental model and the actual state of the robot. We present a grounded word-learning system with the Pepper robot which learns object and action labels and investigate two extensions geared towards increasing the system&#x2019;s transparency. The first extension utilizes deictic gestures (pointing and gaze) to communicate knowledge about object names and to further request new labels. The second extension shows the current state of the lexicon on the robot&#x2019;s tablet. We performed a user study (n=32) to investigate the effects of the transparency methods on learning performance and teaching behavior. In a quantitative analysis, we did not see a significant performance increase for the two extensions. However, users reported higher perception of control and perceived learning success the better they knew the current state of the learning system. In a qualitative analysis, we investigated the participants' teaching behaviors and identified factors that inhibited the learning process. Among other things, we found increased interactive behavior of users when the robot displayed deictic gestures. We saw that human tutors simplified their utterances over time to adapt to the perceived capabilities of the robot. The tablet was most helpful for users to understand what the robot had already learned. Still, learning was impaired in all conditions, when the human input substantially deviated from the form required by the learning system.
Efficient Classification Techniques in Sentiment Analysis Using Transformers

Classification in sentiment analysis using transformers is a state-of-the-art method. The transformer model speeds up the training process with the help of an attention mechanism. The encoding and decoding architecture in transformers helps in language modeling for machine translation and text summarization. The pre-trained transformer model provides a trained model for downsampling to private datasets of various sizes. In this paper, we conduct a study on efficient classification techniques using transformers. The fine-tuning task of transformers is the focus of our work. The choices of hyperparameters for fine-tuning have a significant impact on the final results. Early stopping method for regularization eliminates overfitting during training time. The performance of the efficient transformer models BERT, RoBERTa, ALBERT, and DistillBERT has been analyzed and compared with Long Short-Term Memory and experimental results confirm the superiority of these models over LSTM.
Image Denoising Using a Nonlinear Pixel-Likeness Weighted-Frame Technique

Recent advances in the development of image denoising applications for eliminating the various sources of noise in digital images have employed hardware platforms based on field programmable gate arrays for attaining speed and efficiency, which are essential factors in real-time applications. However, image denoising providing for maximum denoising performance, speed, and efficiency on these platforms is subject to constant innovation. To this end, the present work proposes a high-throughput fixed-point adaptive edge noise filter architecture to denoise digital images with additive white Gaussian noise in realtime using a nonlinear modified pixel-likeness weighted-frame technique. The proposed architecture works in two stages. The first stage involves normal and conditional sorting. The second stage is a decision-oriented output selection unit. Decision-oriented adaptive windowing is included for better impulse noise suppression and edge preservation. The denoising performance of the proposed denoising scheme is demonstrated to be superior to those currently available state-of-the-art approaches. Moreover, the power consumption is reduced by 25.01% compared to conventional algorithms.
Detecting concurrency vulnerabilities based on partial orders of memory and thread events

Memory vulnerabilities are the main causes of software security problems. However, detecting vulnerabilities in multi-threaded programs is challenging because many vulnerabilities occur under specific executions, and it is hard to explore all possible executions of a multi-threaded program. Existing approaches are either computationally intensive or likely to miss some vulnerabilities due to the complex thread interleaving. This paper introduces a novel approach to detect concurrency memory vulnerabilities based on partial orders of events. A partial order on a set of events represents the definite execution orders of events. It allows constructing feasible traces exposing specific vulnerabilities by exchanging the execution orders of vulnerability-potential events. It also reduces the search space of possible executions and thus improves computational efficiency. We propose new algorithms to extract vulnerability-potential event pairs for three kinds of memory vulnerabilities. We also design a novel algorithm to compute a potential event pair's feasible set, which contains the relevant events required by a feasible trace. Our method extends existing approaches for data race detection by considering that two events are protected by the same lock. We implement a prototype of our approach and conduct experiments to evaluate its performance. Experimental results show that our tool exhibits superiority over state-of-the-art algorithms in both effectiveness and efficiency.
Integrating RoBERTa Fine-Tuning and User Writing Styles for Authorship Attribution of Short Texts

Authorship Attribution (AA) is a fundamental branch of text classification, aiming at identifying the authors of given texts. However, authorship attribution of short texts faces many challenges like short text, feature sparsity and non-standardization of casual words. Recent studies have shown that deep learning methods can greatly improve the accuracy of AA tasks, however they still represent user posts using a set of predefined features (e.g., word n-grams and character n-grams) and adopt text classification methods to solve this task. In this paper, we propose a hybrid model to solve author attribution of short texts. The first part is a pretrained language model based on RoBERTa to produce post representations that are aware of tweet-related stylistic features and their contextualities. The second part is a CNN model built on a number of feature embeddings to represent users' writing styles. Finally, we assemble these representations for final AA classification. Our experimental results show that our model on tweets shows the state-of-the-art result on a known tweet AA dataset .
Optimized Chaotic encrypted image based on continuous raster scan method

As the greatest effective method of protection of multimedia data is image encryption is extensively used in data hiding, security authentication and content protection. Nevertheless, the security and efficiency are still the key issues of image encryption algorithm. However, the existing image encryption techniques face the issues namely poor resistance to plaintext attacks, simple algorithmic structures and small key space. In order to overcome the problem of traditional techniques, a joint image encryption techniques of continuous raster Scan, Henon Chaotic Map and Chebyshev Chaotic Map are developed for providing a security of images. Initially, the continuous raster is used to scramble the pixels of each blocks, where images are partitioning an image into non overlapping blocks and starting point is randomly selected in Henon Chaotic Map to permute the block. To calculate a random matrix, Chebyshev Chaotic Map is used to generate the sequence based on the secret keys. Finally, bitwise XOR operation between the scrambled image and random matrix is conducted to obtain the encrypted image. The proposed method takes very less time for encryption. Simulation results show that the algorithm is efficient and usable for the security of the image encryption system.
Going Shallower with MobileNets: On the Impact of Wavelet Pooling

MobileNet is a light-weight neural network model that has facilitated harnessing the power of deep learning on mobile devices. The advances in pervasive computing and the ever-increasing interest in deep learning has resulted in a growing research attention on the enhancement of the MobileNet architecture. Beside the enhancement in convolution layers, recent literature has featured new directions for implementing the pooling layers. In this work, we propose a new model based on the MobileNet-V1 architecture, and we investigate the impact of wavelet pooling on the performance of the proposed model. While traditional neighborhood pooling can result in information loss, which negatively impacts any succeeding feature extraction, wavelet pooling allows us to utilize spectral information which is useful in most image processing tasks. On two widely adopted datasets, we evaluated the performance of the proposed model, and compared to the baseline MobileNet, we attained a 10% and a 16% increase in classification accuracy on CIFAR-10 and CIFAR-100 respectively. We also evaluated a shallow version of the proposed architecture with wavelet pooling, and we showed that it maintained the classification accuracy either higher than, or <; 1% less than, the deep versions of MobileNet while decreasing the number of model parameters by almost 40%.