Reviewerly/exHarmony huggingface dataset:
Outlier detection from multiple data sources

The preparation of a dataset by merging multiple data sources using the data fusion method may lead to the loss of vital information from each multi-source dataset and a certain amount of correlative information among the multiple data sources. Based on the extensive analysis of "the unique characteristics" of multi-source outliers, we propose multi-source outlier detection techniques to reliably identify outliers in multiple datasets. Several real-world examples are considered to classify multi-source outliers into three types (Type I-III) depending on the correlation among datasets. We design a baseline algorithm, which is an intuitive solution, and an optimal algorithm known as multiple-data-sources oriented outlier detection (MOD) to obtain high-score outliers. In addition, we build the MOD+ method to speed up the outlier detection process. A new density metric combining kNN and RNN is introduced to evaluate the deviation degrees of multi-source outliers. The new outlier are applied to develop three outlier-join operators. MOD and MOD+ are adept at (1) mining outlier information from each one of multi-source datasets and (2) sensing correlative outlier information among these multiple datasets. We implement and evaluate the three outlier detection algorithms by using synthetic and real-world datasets. The experimental results demonstrate that the proposed methods are promising and practical in the context of detecting outliers from multi-source datasets.
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Determinants with Legendre symbol entries have close relations with character sums and elliptic curves over finite fields. In recent years, Sun [16], Krachun and his cooperators [11] studied this topic. In this paper, we confirm some conjectures posed by Sun and investigate some related topics. For instance, given any integers c,d with d≠0 and c2−4d≠0, we show that there are infinitely many odd primes p such thatdet⁡[(i2+cij+dj2p)]0≤i,j≤p−1=0, where (⋅p) is the Legendre symbol. This confirms a conjecture of Sun.
User behavior pattern mining and reuse across similar Android apps

Nowadays, Android apps have penetrated all aspects of our lives. Despite their popularity, understanding their behaviors is still a challenging task. Considering that many Android apps are in the same category and share similar workflows, in this paper, we propose a user behavior pattern mining and reuse approach across similar Android apps, thereby reducing the cost of understanding new apps. Particularly, for a specific new app, to figure out its typical behaviors, the behavior patterns that refer to the frequently-occurring workflows can be obtained from another similar app and transferred to this app. Moreover, to reuse the behavior patterns on this app, a semantic-based event fuzzy matching strategy and continuous workflow generation strategy are raised to generate workflows for this app. To evaluate our approach’s effectiveness and rationality, we conduct a series of experiments on 25 Android apps in five categories. Furthermore, the experimental results show that 88.3% of behavior patterns can be completely reused on similar apps, and the generated workflows cover 89.1% of the top 20% of important states.
Image-to-video person re-identification using three-dimensional semantic appearance alignment and cross-modal interactive learning

Image-to-video person re-identification (I2V ReID), which aims to retrieve human targets between image-based queries and video-based galleries, has recently become a new research focus. However, the appearance misalignment and modality misalignment in both images and videos caused by pose variations, camera views, misdetections, and different data types, make I2V ReID still challenging. To this end, we propose a deep I2V ReID pipeline based on three-dimensional semantic appearance alignment (3D-SAA) and cross-modal interactive learning (CMIL) to address the aforementioned two challenges. Specifically, in the 3D-SAA module, the aligned local appearance images extracted by dense 3D human appearance estimation are in conjunction with global image and video embedding streams to learn more fine-grained identity features. The aligned local appearance images are further semantically aggregated by the proposed multi-branch aggregation network to weaken the negligible body parts. Moreover, to overcome the influence of modality misalignment, a CMIL module enables the communication between global image and video streams by interactively propagating the temporal information in videos to the channels of image feature maps. Extensive experiments on challenging MARS, DukeMTMC-VideoReID and iLIDS-VID datasets, show the superiority of our approach.
A risk-level assessment system based on the STRIDE/DREAD model for digital data marketplaces

Abstract Security is a top concern in digital infrastructure and there is a basic need to assess the level of security ensured for any given application. To accommodate this requirement, we propose a new risk assessment system. Our system identifies threats of an application workflow, computes the severity weights with the modified Microsoft STRIDE/DREAD model and estimates the final risk exposure after applying security countermeasures in the available digital infrastructures. This allows potential customers to rank these infrastructures in terms of security for their own specific use cases. We additionally present a method to validate the stability and resolution of our ranking system with respect to subjective choices of the DREAD model threat rating parameters. Our results show that our system is stable against unavoidable subjective choices of the DREAD model parameters for a specific use case, with a rank correlation higher than 0.93 and normalised mean square error lower than 0.05.
Developing a Platform of Personalized Conversation Scenarios for In-home Care Assistance

The burden on family caregivers for in-home care is increasing in a super-aging society. Realizing conversational agents adapted to individual situations for in-home care assistance is a hard technical issue. The purpose of this paper is to realize a method that can create a personalized conversational agent by the end-user. The challenging points of this paper include two aspects: (1) Editing conversation scenarios easier for end-users. (2) Sharing edited conversation scenarios with others. As the approach, we first present a platform of personalized conversation scenario (PopCS). Then, we create a logs-as-scenarios concept to improve the personalized contents and the edited efficiency. By selectively sharing the conversation logs, we build a connection between the local conversational agent and the remote that seems like telemedicine. Since different family caregivers and healthcare specialists join in it, continuing to improve the conversation scenarios for in-home care assistance is promising.
Digital Watermark Perturbation for Adversarial Examples to Fool Deep Neural Networks

In this paper we propose an attack method to embed digital watermarking invisibly into a clean example to generate an adversarial example to interfere with the classification of deep learning models. Specifically, we propose an optimization algorithm called Non-Dominated Sorting Genetic Algorithm with Particle Swarm Optimization (NSGA-PSO) to generate adversarial digital watermarking in the black-box attack mode with a few queries from the models to be attacked. Extensive experiments on ImageNet and CIFAR-10 datasets demonstrate that our method can efficiently generate adversarial examples with higher attack success rates than existing black-box attack methods. Furthermore, showing satisfactory transferability across different network models and greater robustness against image transformation defense methods.
A Deep Local Patch Matching Network for Cell Tracking in Microscopy Image Sequences Without Registration

Cell tracking is critical for the modeling of plant cell growth patterns. A local graph matching algorithm is proposed to track cells by exploiting the tight spatial topology of cells. However, the local graph matching approach lacks robustness in the unregistered images because the feature descriptors are handcrafted. In this paper, we propose a Deep Local Patch Matching Network (DLPM-Net) to track cells robustly, by exploiting local patches' deep similarity information and cells' spatial-temporal contextual information. Furthermore, to reduce the time consumption during the matching process and enhance tracking accuracy, we take two steps to realize the tracking of non-division cells and the detection of cell divisions. In the first step, the DLPM-Net is employed to match the non-division cells by exploiting the cell pair candidates' local patch contextual information, then the non-matched cells are recorded as the cell division candidates. In the second step, the DLPM-Net is used to detect cell divisions from these non-matched cells, by exploiting the local patch contextual similarity between the mother cell's local patch and daughter cells' local patch. Compared with the existing local graph matching method, the experimental results show that the proposed method gains 29.1% improvement in the tracking accuracy.
Code Change Sniffer: Predicting Future Code Changes with Markov Chain

Code changes are one of the essential processes of software evolution.These changes are performed to fix bugs, improve quality of software, and provide a better user experience.However, such changes made in code could lead to ripple effects that can cause unwanted behavior.To prevent such issues occurring after code changes, code change prediction, change impact analysis techniques are used.The proposed approach uses static call information, forward slicing, and method change information to build a Markov chain, which provides a prediction for code changes in the near future commits.For static call information, we utilized and compared call graph and effect graph.We performed an evaluation on five open-source projects from GitHub that varies between 5K-26K lines of code.To measure the effectiveness of our proposed approach, recall, precision, and f-measure metrics have been used on five opensource projects.The results show that the Markov chain that is based on call graph can have higher precision compared to effect graph.On the other hand, for small number of cases higher recall values are obtained with effect graph compared to call graph.With a Markov chain model based on call graph and effect graph, we can achieve recall values between 98%-100%.
Mixture of Experts of Neural Networks and Kalman Filters for Optical Belt Sorting

In optical sorting of bulk material, the composition of particles may frequently change. State-of-the-art sorting approaches rely on tuning physical models of the particle motion. The aim of this work is to increase the prediction accuracy in complex fast-changing sorting scenarios with data-driven approaches. In this article, we propose two neural network (NN) experts for accurate prediction of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> known particle types. To handle the large variety of particle types that can occur in real-world sorting scenarios, we introduce a simple but effective mixture of experts’ approach that combines NNs with hand-crafted motion models. Our new method not only improves the prediction accuracy for bulk material consisting of many particle classes, but also proves to be very adaptive and robust to new particle types.
Characterizing the evolution of the Yangtze River Delta multi-port system using compositional data techniques

The Yangtze River Delta multi-port system (YMPS) receives increasing attention due to its relevance in world trade and excellent competitiveness in the container traffic market. To insight into the development pattern of YMPS, this paper proposes a method that combines the Hierarchical Clustering with compositional data (CoDa) exploratory tools (i.e., biplot and dendrogram) to explore the temporal and spatial evolution of the YMPS from 1992 to 2019. CoDa describes parts of some whole (i.e., frequency and percentage), conveying relative information in the ratios between its components. Container traffic share in a multi-port region is typical CoDa. Traditional statistical approaches to CoDa could lead to spurious correlations and erroneous conclusions. However, using suitable CoDa techniques, such as the centered log-ratio (clr) transformation, can effectively avoid these misinterpretations. The novel method can simultaneously find the temporal and spatial characteristics. The findings indicate that the development of the YMPS has gone through four stages and the evolution of the YMPS is characterized by a tendency towards a ‘multi-core development’ and faces a differentiated pattern of ‘peripheral port challenges’. The analysis further improves the port system’s evolutionary model and explains the underlying reason for the YMPS development. CoDa techniques also provide a new perspective for the temporal and spatial evolution of the transport discipline.
Emotion Dynamics Modeling via BERT

Emotion dynamics modeling is a significant task in emotion recognition in conversation. It aims to predict conversational emotions when building empathetic dialogue systems. Existing studies mainly develop models based on Recurrent Neural Networks (RNNs). They cannot benefit from the power of the recently-developed pre-training strategies for better token representation learning in conversations. More seriously, it is hard to distinguish the dependency of interlocutors and the emotional influence among interlocutors by simply assembling the features on top of RNNs. In this paper, we develop a series of BERT-based models to specifically capture the inter-interlocutor and intra-interlocutor dependencies of the conversational emotion dynamics. Concretely, we first substitute BERT for RNNs to enrich the token representations. Then, a Flat-structured BERT (F-BERT) is applied to link up utterances in a conversation directly, and a Hierarchically-structured BERT (H-BERT) is employed to distinguish the interlocutors when linking up utterances. More importantly, a Spatial-Temporal-structured BERT, namely ST-BERT, is proposed to further determine the emotional influence among interlocutors. Finally, we conduct extensive experiments on two popular emotion recognition in conversation benchmark datasets and demonstrate that our proposed models can attain around 5% and 10% improvement over the state-of-the-art baselines, respectively.
Chinese causal event extraction using causality‐associated graph neural network

Abstract Causal event extraction (CEE) aims to identify and extract cause‐effect event pairs from texts, which is a fundamental task in natural language processing. Recent research treat CEE as a sequence labeling problem. However, the linguistic complexity and ambiguity of textual description results in the low accuracy of extractors. To address the above issues, considering the prior knowledge like the causal network constructed based on the causal indicators, which can represent information transition between cause and effect, may helpful for CEE. In this article, we propose causality‐associated graph neural network to incorporate in‐domain knowledge by taking important causal words into account. External causal knowledge is modeled as causal associated graph (CAG). Then we use graph neural networks (GNN) to capture the complex relationship of intraevent mentions and interevent causality in a sentence based on the relationship obtained from CAG. Finally, sentence sequence and prior causal knowledge of GNN embedding are fed into multiscaled convolution and bidirectional long short‐term memory networks. Experimental results on two datasets show that our method outperforms the state‐of‐the‐art baseline.
Image Schemas and Conceptual Blending in Diagrammatic Reasoning: The Case of Hasse Diagrams

Abstract In this work, we propose a formal, computational model of the sense-making of diagrams by using the theories of image schemas and conceptual blending, stemming from cognitive linguistics. We illustrate our model here for the case of a Hasse diagram, using typed first-order logic to formalise the image schemas and to represent the geometry of a diagram. The latter additionally requires the use of some qualitative spatial reasoning formalisms. We show that, by blending image schemas with the geometrical configuration of a diagram, we can formally describe the way our cognition structures the understanding of, and the reasoning with, diagrams. In addition to a theoretical interest for diagrammatic reasoning, we also briefly discuss the cognitive underpinnings of good practice in diagram design, which are important for fields such as human-computer interaction and data visualization.
Medical named entity recognition of Chinese electronic medical records based on stacked Bidirectional Long Short-Term Memory

The wide adoption of electronic medical record (EMR) systems causes rapid growth of medical and clinical data. It makes the medical named entity recognition (NER) technologies become critical to find useful patient information in the medical dataset. However, the medical terminologies usually have the characteristics of inherent complexity and ambiguity, it is difficult to capture context-dependency representations by supervision signal from a simple single layer structure model. In order to address this problem, this paper proposes a hybrid model based on stacked Bidirectional Long Short-Term Memory (BILSTM) for medical named entity recognition, which we call BSBC (BERT combined with stacked BILSTM and CRF). First, we use Bidirectional Encoder Representation from Transformers (BERT) to perform unsupervised learning on an unlabeled dataset to obtain character-level embeddings. Then, stacked BILSTM is utilized to obtain context-dependency representations through the multi hidden layers structure. Finally, Conditional Random Field (CRF) is used to predict sequence tags. The experiment results show that our method significantly outperforms the baseline methods, it serves as a strong alternative approach compared with traditional methods.
Cognition‐Enhanced Machine Learning for Better Predictions with Limited Data

The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields' methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance based on learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can be enhanced by incorporating insights from a cognitive model of human memory. This was done by exploiting the predictive performance equation's (PPE) narrow but highly specialized domain knowledge with regard to the temporal dynamics of learning and forgetting. Specifically, the PPE was used to engineer timing-related input features for a gradient-boosted decision trees (GBDT) model. The resulting PPE-enhanced GBDT outperformed the default GBDT, especially under conditions in which limited data were available for training. Results suggest that integrating cognitive and ML models could be particularly productive if the available data are too high-dimensional to be explained by a cognitive model but not sufficiently large to effectively train a modern ML algorithm. Here, the cognitive model's insights pertaining to only one aspect of the data were enough to jump-start the ML model's ability to make predictions-a finding that holds promise for future explorations.
Research and Application of Back Propagation Neural Network-Based Linear Constrained Optimization Method

A back propagation (BP) neural network-based linear constrained optimization method(BPNN-LCOM) was proposed for to solve the problems in linear constraint black box in this paper,hoping to improve the shortcoming of BP neural network-based constrained optimization method (BPNN-COM).In view of minimizing the mathematic model of network output, the basic ideas of BPNN-LCOM wereilluminated,includingmodel design and training, and BP neural network-based global optimization.Firstly, the iteration step size was calculated by optimal step size, and the adjustment step size was calculated by interpolation method, also the iteration speed was accelerated.Secondly, the search direction that iteration point locates on the boundary offeasible region was determined by gradient projection method, which ensured that the iteration process continued along a feasible search direction, and effectively solved the defect of BPNN-COM that sometimes fails to find thetrue optimal solutions.At the same time, the iteration step size along the gradient projection direction was calculated by the optimal constraint step size, which ensured the new iteration point located in the feasible region.Thirdly, the Kuhn-Tucker conditions were introduced to verify whether the iteration point is theoptimization solution that locates on the boundary of feasible region, and it made the termination criterion perfect for BPNN-LCOM.The computation results of two examples showed the effectiveness and feasibility of BPNN-LCOM.The BPNN-LOCM was used to optimize the roller-type bailing mechanism,and the optimal parameters were obtained as follows: round disc diameter was 360 mm, rotationalspeed of the steel rollerwas 250 rpm, feeding quantity was1.7 kg/s, and length-width ratio was 0.8.The corresponding minimum power consumption was 45.8 kJ/bundle.The optimization results were superior to regression analysis and BPNN-COM.The verification test was carried out and the optimization results could improve roller-type bailing mechanism.Verification results showed that the BPNN-LCOM is a feasible method for solving problems in linear constraint black box.
Generalization and completeness of stochastic local search algorithms

We generalize Stochastic Local Search (SLS) heuristics into a unique formal model. This model has two key components: a common structure designed to be as large as possible and a parametric structure intended to be as small as possible. Each heuristic is obtained by instantiating the parametric part in a different way. Particular instances for Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) are presented. Then, we use our model to prove the Turing-completeness of SLS algorithms in general. The proof uses our framework to construct a GA able to simulate any Turing machine. This Turing-completeness implies that determining any non-trivial property concerning the relationship between the inputs and the computed outputs is undecidable for GA and, by extension, for the general set of SLS methods (although not necessarily for each particular method). Similar proofs are more informally presented for PSO and ACO.