Reviewerly/exHarmony huggingface dataset:
ParChain

This paper studies the hierarchical clustering problem, where the goal is to produce a dendrogram that represents clusters at varying scales of a data set. We propose the ParChain framework for designing parallel hierarchical agglomerative clustering (HAC) algorithms, and using the framework we obtain novel parallel algorithms for the complete linkage, average linkage, and Ward's linkage criteria. Compared to most previous parallel HAC algorithms, which require quadratic memory, our new algorithms require only linear memory, and are scalable to large data sets. ParChain is based on our parallelization of the nearest-neighbor chain algorithm, and enables multiple clusters to be merged on every round. We introduce two key optimizations that are critical for efficiency: a range query optimization that reduces the number of distance computations required when finding nearest neighbors of clusters, and a caching optimization that stores a subset of previously computed distances, which are likely to be reused. Experimentally, we show that our highly-optimized implementations using 48 cores with two-way hyper-threading achieve 5.8--110.1x speedup over state-of-the-art parallel HAC algorithms and achieve 13.75--54.23x self-relative speedup. Compared to state-of-the-art algorithms, our algorithms require up to 237.3x less space. Our algorithms are able to scale to data set sizes with tens of millions of points, which existing algorithms are not able to handle.
Fuzzy Cognitive Maps as a Tool for Identifying Cognitive Conflicts That Hinder the Adoption of Healthy Habits

Implementing healthy lifestyle habits can take a great effort and sticking to such prescriptions is complicated. Failure rates amongst people seeking to adopt a healthier diet are estimated to be around 80%. Exploring the network of meanings that an individual associates with adopting habits such as healthy eating, maintaining the correct weight, and practising physical exercise can reveal the inconsistencies, obstacles, or psychological conflicts that hinder change and target-achievement. Fuzzy cognitive maps (FCM) can be of great utility in this task as they allow us to explore the structure of the personal meaning system of an individual as well as determine any obstacles and simulate hypothetical scenarios that project its future evolution. This can help to identify the foci of cognitive conflicts that hinder the adoption of healthy habits and establish more effective personalised intervention programmes that make it easier to maintain these habits.
Momentum source-proxy guided initialization for unsupervised domain adaptive person re-identification

Unsupervised domain adaptive person re-identification (UDA Re-ID), aiming to adapt the model trained from source domain to target domain, is especially challenging due to the non-overlapping identities between the two Re-ID domains. State-of-the-art UDA Re-ID methods optimize the model pre-trained on source domain with pseudo labels generated by clustering algorithms on the target domain. The drawback lies in that the initial parameters are learned only from labeled source domain, neglecting the target domain information that can be easily obtained from unlabeled data. In order to better fit the target distribution while preventing from over-fitting to the source one, we propose a novel momentum source-proxy guided initialization (MSPGI) approach to integrate information from unlabeled data into the pre-training process. Specifically, we assign soft labels to unlabeled data according to similarity to the feature proxies of the source domain, based on the finding that different Re-ID datasets share commonalities. In addition, we instantiate the pretext task in unsupervised pre-training as constraining the predicted soft label to be consistent with the one calculated from the temporally-averaged parameters of the model. Experiments are conducted on multiple downstream approaches, pushing forward the state-of-the-art results by an impressive margin on Market-1501 and DukeMTMC-reID. By making use of unlabeled data, MSPGI further improves the performance of a fully supervised network.
Recommender System for Information Retrieval Using Natural Language Querying Interface Based in Bibliographic Research for Na&amp;#239;ve Users

With the increasing of data on the internet, data analysis has become inescapable to gain time and efficiency, especially in bibliographic information retrieval systems.We can estimate the number of actual scientific journals points to around 40,000 with about four million articles published each year.Machine learning and deep learning applied to recommender systems had become unavoidable whether in industry or in research.In this current, we propose an optimized interface for bibliographic information retrieval as a running example, which allows different kind of researchers to find their needs following some relevant criteria through natural language understanding.Papers indexed in Web of Science and Scopus are in high demand.Natural language including text and linguistic-based techniques, such as tokenization, named entity recognition, syntactic and semantic analysis, are used to express natural language queries.Our Interface uses association rules to find more related papers for recommendation.Spanning trees are challenged to optimize the search process of the system.
Simulating an all-optical quantum controlled-NOT gate using soliton scattering by a reflectionless potential well

We present a protocol for the quantum controlled-NOT gate which is based on two qubits operation by investigating the soliton scattering through a reflectionless potential well in an optical system. We consider the set up of two input solitons with different intensities scattered by a reflectionless potential well with a control soliton placed at the center of the potential. The two input solitons correspond to the target qubit while either the presence or absence of control soliton in the potential well or the presence or absence of control potential well corresponds to the control qubit. We achieve the desired performance of the quantum logic gate by exploiting the intensity difference between the two input solitons and we find this to be possible within a finite width of a velocity of incidence for the two solitons. The calculation of transport coefficients ensures the feasibility of building a quantum controlled-NOT gate. This protocol demonstrates the prospect of soliton scattering by a potential well for quantum information processing. Especially, the setup with control potential as a control qubit allows realization of the CNOT operation with the negligible amount of radiation.
Research on English Achievement Analysis Based on Improved CARMA Algorithm

This paper uses data mining technology to analyze students' English scores. In view of the influence of many factors on students' English performance, the analysis is realized by using the association rule algorithm. The thesis analyzes and applies students' English scores based on association rules and mainly does the following work: (1) at present, the problem of the CARMA algorithm is low operating efficiency. The combination of the genetic algorithm's crossover, mutation, and the CARMA algorithm realizes the fast search of the algorithm. The simulation results show that the operation performance of the algorithm is greatly improved after the crossover and mutation operations in the genetic algorithm are applied to the CARMA algorithm. The simulation results show that the mining accuracy of the improved algorithm is 97.985%, and the mining accuracy before the improvement is 92.221%, indicating that the improved algorithm can improve the accuracy of mining. (2) By comparing the mining time of the improved CARMA algorithm, the traditional CARMA algorithm, the FP-Growth algorithm, and the Apriori algorithm, the results show that when the number is 6,500, the mining efficiency of the improved CARMA algorithm is twice that of the other three algorithms. As the amount of data increases, the effect of improving mining efficiency gradually increases. (3) By using the improved CARMA algorithm to analyze students' English performance, it is found that the quality of student performance is strongly related to the quality of daily homework, and if it is related to the teacher's gender, professional title, etc., it is recommended that schools should pay more attention to homework during the teaching process.
Instance segmentation convolutional neural network based on multi-scale attention mechanism

Instance segmentation is more challenging and difficult than object detection and semantic segmentation. It paves the way for the realization of a complete scene understanding, and has been widely used in robotics, automatic driving, medical care, and other aspects. However, there are some problems in instance segmentation methods, such as the low detection efficiency for low-resolution objects and the slow detection speed of images with complex backgrounds. To solve these problems, this paper proposes an instance segmentation method with multi-scale attention, which is called a Hybrid Kernel Mask R-CNN. Firstly, the hybrid convolution kernel is constructed by combining different kernels and groups, which can complement each other to extract rich information. Secondly, a multi-scale attention mechanism is designed by assign weights to different convolution kernels, which can retain more important information. After the introduction of our strategy, the network is more inclined to focus on the low-resolution objects in the image. The proposed method achieves the best accuracy over the anchor-based method. To verify the universality of the model, we test Hybrid Kernel Mask R-CNN on Balloon, xBD and COCO datasets. The test results exceed the state of art methods. And the visualization results show our method can extract low-resolution objects effectively.
Subgraph matching over graph federation

Many real-life applications require processing graph data across heterogeneous sources. In this paper, we define the graph federation that indicates that the graph data sources are temporarily federated and offer their data for users. Next, we propose a new framework FedGraph to efficiently and effectively perform subgraph matching, which is a crucial application in graph federation. FedGraph consists of three phases, including query decomposition, distributed matching, and distributed joining. We also develop new efficient approximation algorithms and apply them in each phase to attack the NP-hard problem. The evaluations are conducted in a real test bed using both real-life and synthetic graph datasets. FedGraph outperforms the state-of-the-art methods, reducing the execution time and communication cost by 37.3 × and 61.8 ×, respectively.
MFFNet: Single facial depth map refinement using multi-level feature fusion

Depth maps have been used in many vision tasks due to the real-time acquisition and low cost of consumer depth cameras. However, they still suffer from low precision and severe sensor noise, even with the significant research in depth enhancement. We propose a novel multi-level feature fusion convolutional neural network (CNN) for facial depth map refinement named MFFNet. It is a multi-stage network, where each stage is a local multi-level feature fusion (LMLF) block. For smoothing the noise as well as boosting detailed facial structure, a hierarchical fusion strategy is adopted to fully fuse multi-level features, i.e., an LMLF block fuses multi-level features locally in each stage, while inter-stage skip connections are employed to reach a global multi-level feature fusion. Moreover, the inter-stage skip connections can also ease the training through shortening the information propagation paths. We introduce an effective data augmentation method to synthesize noisy facial depth maps of various poses. Training with these synthetic data improves the robustness of the proposed method to face poses. The proposed method is evaluated with a synthetic facial depth map dataset, a real Kinect V2 facial depth map dataset and the Middlebury Stereo Dataset. Experimental results show that our method produces refined depth maps with high quality and outperforms several state-of-the-art methods.
An Improved Authentication Scheme for Digital Rights Management System

With the increasing number and popularity of digital content, the management of digital access rights has become an utmost important field. Through digital rights management systems (DRM-S), access to digital contents can be defined and for this, an efficient and secure authentication scheme is required. The DRM authentication schemes can be used to give access or restrict access to digital content. Very recently in 2020, Yu et al. proposed a symmetric hash and xor-based DRM and termed their system to achieve both security and performance efficiency. Contrarily, in this study, we argue that their scheme has several issues including nonresistance to privileged insider and impersonation attacks. Moreover, it is also to show in this study that their scheme has an incorrect authentication phase and due to this incorrectness, the scheme of Yu et al. lacks user scalability. An improved scheme is then proposed to counter the insecurities and incorrectness of the scheme of Yu et al. We prove the security of the proposed scheme using BAN logic. For a clear picture of the security properties, we also provide a textual discussion on the robustness of the proposed scheme. Moreover, due to the usage of symmetric key-based hash functions, the proposed scheme has a comparable performance efficiency.
Bitcoin speculation, investor attention and major events. Are they connected?

ABSTRACTABSTRACTWe use a time-varying vector autoregressive model to investigate the dynamic effect of investor attention on Bitcoin speculation and then examine the association of this effect with five types of events in the Bitcoin market. The results indicate that investor attention has a positive effect on Bitcoin speculation and this effect changes with time and decays as lag phases increase. Policy-related events are the key factors that make this effect time-varying, while safety events have no obvious impact. Besides, our results find the existence of contrarian strategy in the Bitcoin market.KEYWORDS: Bitcoininvestor attentionspeculative tradingTVP-SV-VARJEL CLASSIFICATION: G12G14 AcknowledgmentsThis research is supported by the National Natural Science Foundation of China (No. 72173018).Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China [72173018].
Efficient Realization of Decision Trees for Real-Time Inference

For timing-sensitive edge applications, the demand for efficient lightweight machine learning solutions has increased recently. Tree ensembles are among the state-of-the-art in many machine learning applications. While single decision trees are comparably small, an ensemble of trees can have a significant memory footprint leading to cache locality issues, which are crucial to performance in terms of execution time. In this work, we analyze memory-locality issues of the two most common realizations of decision trees, i.e., native and if-else trees. We highlight that both realizations demand a more careful memory layout to improve caching behavior and maximize performance. We adopt a probabilistic model of decision tree inference to find the best memory layout for each tree at the application layer. Further, we present an efficient heuristic to take architecture-dependent information into account thereby optimizing the given ensemble for a target computer architecture. Our code-generation framework, which is freely available on an open-source repository, produces optimized code sessions while preserving the structure and accuracy of the trees. With several real-world data sets, we evaluate the elapsed time of various tree realizations on server hardware as well as embedded systems for Intel and ARM processors. Our optimized memory layout achieves a reduction in execution time up to 75 % execution for server-class systems, and up to 70 % for embedded systems, respectively.
Using Hyperledger Fabric to Reduce Fraud in International Trade

Due to the physical distances and different global customs and laws, international trade is fraught with risk; a significant threat involves falsified or fraudulent letters of credit (LC) and/or bills of lading (BoL). We describe how federated and permissioned blockchains are more suitable to detect and mitigate fraudulent behavior than permissionless ones. We propose a framework based on Hyperledger Fabric that can ensure that the involved entities are valid, transactions on these goods are correctly ordered, documents are verifiable, and they properly represent the goods on the BoL Next, we discuss three scenarios that illustrate fraud in international trade and describe how our proposed framework can address each of these concerns. Last, we discuss some challenges in implementing our proposed framework and discuss future steps in our research.
TAB: Unified and Optimized Ternary, Binary, and Mixed-precision Neural Network Inference on the Edge

Ternary Neural Networks (TNNs) and mixed-precision Ternary Binary Networks (TBNs) have demonstrated higher accuracy compared to Binary Neural Networks (BNNs) while providing fast, low-power, and memory-efficient inference. Related works have improved the accuracy of TNNs and TBNs, but overlooked their optimizations on CPU and GPU platforms. First, there is no unified encoding for the binary and ternary values in TNNs and TBNs. Second, existing works store the 2-bit quantized data sequentially in 32/64-bit integers, resulting in bit-extraction overhead. Last, adopting standard 2-bit multiplications for ternary values leads to a complex computation pipeline, and efficient mixed-precision multiplication between ternary and binary values is unavailable. In this article, we propose TAB as a unified and optimized inference method for ternary, binary, and mixed-precision neural networks. TAB includes unified value representation, efficient data storage scheme and novel bitwise dot product pipelines on CPU/GPU platforms. We adopt signed integers for consistent value representation across binary and ternary values. We introduce a bitwidth-last data format that stores the first and second bits of the ternary values separately to remove the bit extraction overhead. We design the ternary and binary bitwise dot product pipelines based on Gated-XOR using up to 40% fewer operations than State-Of-The-Art (SOTA) methods. Theoretical speedup analysis shows that our proposed TAB-TNN is 2.3× fast as the SOTA ternary method RTN, 9.8× fast as 8-bit integer quantization (INT8), and 39.4× fast as 32-bit full-precision convolution (FP32). Experiment results on CPU and GPU platforms show that our TAB-TNN has achieved up to 34.6× speedup and 16× storage size reduction compared with FP32 layers. TBN, Binary-activation Ternary-weight Network (BTN), and BNN in TAB are up to 40.7×, 56.2×, and 72.2× as fast as FP32. TAB-TNN is up to 70.1% faster and 12.8% more power-efficient than RTN on Darknet-19 while keeping the same accuracy. TAB is open source as a PyTorch Extension 1 for easy integration with existing CNN models.
ODIN: Pluggable Meta-annotations and Metrics for the Diagnosis of Classification and Localization

Machine Learning (ML) tasks, especially Computer Vision (CV) ones, have greatly progressed after the introduction of Deep Neural Networks. Analyzing the performance of deep models is an open issue, addressed with techniques that inspect the response of inner network layers to given inputs. A complementary approach relies on ad-hoc metadata added to the input and used to factor the performance into indicators sensitive to specific facets of the data. We present ODIN an open source diagnosis framework for generic ML classification tasks and for CV object detection and instance segmentation tasks that lets developers add meta-annotations to their data sets, compute performance metrics split by meta-annotation values, and visualize diagnosis reports. ODIN is agnostic to the training platform and input formats and can be extended with application- and domain-specific meta-annotations and metrics with almost no coding. It integrates a rapid annotation tool for classification and object detection data sets. In this paper, we exemplify ODIN through CV tasks, but the tool can be used for generic ML classification.
Recent trends in recommendation systems and sentiment analysis

With the rise of technology, anyone can easily share their sentiments through social media platforms like Facebook, Twitter, LinkedIn, Google+, and Instagram. Sentiment analysis is a technique that categorizes opinions from pieces of text to determine a sentiment score (positive, negative, or neutral). Recommendation systems usually aim to match user patterns by finding similar users or different users according to their sentiment scores. Sentiment analysis is dependent on machine learning- or lexicon-based approaches. The following study is an attempt to enlighten how sentiment analysis approaches can be used to improve the outcomes of recommender systems. In the introduction, the basic concepts and terms related to sentiment analysis and recommendation systems are described. Next, we provide an overview of the different aspects of sentiment analysis. Recent developments and related works are discussed subsequently. In this research, we propose a combination of a BERT model and an S3VM classifier using a collaborative recommendation architecture for getting good accuracy. The last part of the chapter discusses challenges and future research directions in this area presents the conclusion.
Improving generation diversity via syntax-controlled paraphrasing

As the neural-based Seq2Seq model pushes the state-of-the-art in text generation, recent work has turned to controlling attributes of the text such models generate, where syntax-controlled text generation can be applied for the paraphrase generation task, i.e., given an input sentence and a syntactic control to generate a paraphrase. The main challenge is how to generate sentences that follow the given syntax while maintaining semantics during the decoding process. Previous approaches using constituency parse trees as syntactic control can achieve better performance, but still suffer from the problems of inaccurate utilization of syntactic information and original semantics loss. To this end, we propose a Syntax Attention-Guided Paraphrase (SAGP) generation model that can utilize the previously generated text to accurately select a syntactic node from the given constituency parse tree to guide the generation of paraphrases. The automatic and manual evaluation results on the public datasets of syntactically controlled paraphrase generation task show that SAGP achieves state-of-the-art results in both syntactic controllability and semantic consistency. In order to improve the semantic consistency, we further propose a coarse-grained syntactic control definition method, which first removes the part-of-speech node and then extracts higher-level subtrees as control, so that meaningful paraphrases can be generated within a loose constraint. The experimental results on the same evaluation set show that the coarse-grained syntactic control can significantly improve semantic consistency.