Reviewerly/exHarmony huggingface dataset:
Blockchain CAP Theorem Allows User-Dependent Adaptivity and Finality

Longest-chain blockchain protocols, such as Bitcoin, guarantee liveness even when the number of actively participating users is variable, i.e., they are adaptive. However, they are not safe under network partitions, i.e., they do not guarantee finality. On the other hand, classical blockchain protocols, like PBFT, achieve finality but not adaptivity. Indeed, the CAP theorem in the context of blockchains asserts that no protocol can simultaneously offer both adaptivity and finality. We propose a new blockchain protocol, called the checkpointed longest chain, that offers individual users the choice between finality and adaptivity instead of imposing it at a system-wide level. This protocol’s salient feature is that it supports two distinct confirmation rules: one that guarantees adaptivity and the other finality. The more optimistic adaptive rule always confirms blocks that are marked as finalized by the more conservative rule, and may possibly confirm more blocks during variable participation levels. Clients (users) make a local choice between the confirmation rules as per their personal preference, while miners follow a fixed block proposal rule that is consistent with both confirmation rules. The proposed protocol has the additional benefit of intrinsic validity: the finalized blocks always lie on a single blockchain, and therefore miners can attest to the validity of transactions while proposing blocks. Our protocol builds on the notion of a finality gadget, a popular technique for adding finality to longest-chain protocols.
Wanted Dead or Alive: Epistemic Logic for Impure Simplicial Complexes

We propose a logic of knowledge for impure simplicial complexes. Impure simplicial complexes represent distributed systems under uncertainty over which processes are still active (are alive) and which processes have failed or crashed (are dead). Our work generalizes the logic of knowledge for pure simplicial complexes, where all processes are alive, by Goubault et al. Our logical semantics has a satisfaction relation defined simultaneously with a definability relation. The latter restricts which formulas are allowed to have a truth value: dead processes cannot know or be uncertain about any proposition, and live processes cannot know or be uncertain about propositions involving processes they know to be dead. The logic satisfies some but not all axioms and rules of the modal logic S5. Impure simplicial complexes correspond to Kripke models where each agent’s accessibility relation is an equivalence relation on a subset of the domain only. We also propose a notion of bisimulation for impure simplicial complexes and show bisimulation correspondence.
Attribute Weighted Na飗e Bayes Classifier

The naïve Bayes classifier is one of the commonly used data mining methods for classification. Despite its simplicity, naïve Bayes is effective and computationally efficient. Although the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning, this assumption may not hold in real-world applications. Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption. While these methods improve the classification performance, they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense of computational time. One approach to reduce the naïveté of the classifier is to incorporate attribute weights in the conditional probability. In this paper, we proposed a method to incorporate attribute weights to naïve Bayes. To evaluate the performance of our method, we used the public benchmark datasets. We compared our method with the standard naïve Bayes and baseline attribute weighting methods. Experimental results show that our method to incorporate attribute weights improves the classification performance compared to both standard naïve Bayes and baseline attribute weighting methods in terms of classification accuracy and F1, especially when the independence assumption is strongly violated, which was validated using the Chi-square test of independence.
Egocentric Biochemical Video-and-Language Dataset

This paper proposes a novel biochemical video-and-language (BioVL) dataset, which consists of experimental videos, corresponding protocols, and annotations of alignment between events in the video and instructions in the protocol. The key strength of the dataset is its user-oriented design of data collection. We imagine that biochemical researchers easily take videos and share them for another researcher’s replication in the future. To minimize the burden of video recording, we adopted an unedited first-person video as a visual source. As a result, we collected 16 videos from four protocols with a total length of 1.6 hours. In our experiments, we conduct two zero-shot video-and-language tasks on the BioVL dataset. Our experimental results show a large room for improvement for practical use even utilizing the state-of-the-art pre-trained video-and-language joint embedding model. We are going to release the BioVL dataset. To our knowledge, this work is the first attempt to release the biochemical video-and-language dataset.
Decision-Making Principles for Better Software Design Decisions

Software design is about making decisions. The quality of design decisions influences the quality of software design. This article describes nine decision-making principles to give software designers a systematic approach for decision making.
Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data. However, the training mechanism for passing model parameters is still threatened by gradient inversion, inference attacks, etc. With a lightweight encryption overhead, function encryption is a viable secure aggregation technique in federation learning, which is often used in combination with differential privacy. The function encryption in federal learning still has the following problems: a) Traditional function encryption usually requires a trust third party (TTP) to assign the keys. If a TTP colludes with a server, the security aggregation mechanism can be compromised. b) When using differential privacy in combination with function encryption, the evaluation metrics of incentive mechanisms in the traditional federal learning become invisible. In this paper, we propose a hybrid privacy-preserving scheme for federated learning, called Fed-DFE. Specifically, we present a decentralized multi-client function encryption algorithm. It replaces the TTP in traditional function encryption with an interactive key generation algorithm, avoiding the problem of collusion. Then, an embedded incentive mechanism is designed for function encryption. It models the real parameters in federated learning and finds a balance between privacy preservation and model accuracy. Subsequently, we implemented a prototype of Fed-DFE and evaluated the performance of decentralized function encryption algorithm. The experimental results demonstrate the effectiveness and efficiency of our scheme.
Puncturable Identity-Based Encryption from Lattices

The concept of puncturable encryption was introduced by Green and Miers at IEEE S&P 2015. Puncturable encryption allows recipients to update their decryption keys to revoke decryption capability for selected messages without communicating with senders. From the first instantiation, puncturable encryption shows its essence for many interesting applications, such as asynchronous messaging systems, group messaging systems, public-key watermarking schemes, secure cloud emails, and many more. To eliminate the necessity of having a costly certificate verification process, Wei et al. introduced puncturable identity-based encryption at ESORICS 2019. Unfortunately, till today, there is no puncturable identity-based encryption which can withstand quantum attacks. In this paper, we aim to fill this gap in the literature by presenting the first constructions of puncturable identity-based encryption, for both selective and adaptive identity, which are secure in the standard model based on the hardness of the learning with errors problem. Design ideas of proposed constructions might prove useful to construct other lattice-based expressive puncturable encryption as well.
Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning

In our previous work, we designed a systematic policy to prioritize sampling locations to lead significant accuracy improvement in spatial interpolation by using the prediction uncertainty of Gaussian Process Regression (GPR) as "attraction force" to deployed robots in path planning. Although the integration with Traveling Salesman Problem (TSP) solvers was also shown to produce relatively short travel distance, we here hypothesise several factors that could decrease the overall prediction precision as well because sub-optimal locations may eventually be included in their paths. To address this issue, in this paper, we first explore "local planning" approaches adopting various spatial ranges within which next sampling locations are prioritized to investigate their effects on the prediction performance as well as incurred travel distance. Also, Reinforcement Learning (RL)-based high-level controllers are trained to adaptively produce blended plans from a particular set of local planners to inherit unique strengths from that selection depending on latest prediction states. Our experiments on use cases of temperature monitoring robots demonstrate that the dynamic mixtures of planners can not only generate sophisticated, informative plans that a single planner could not create alone but also ensure significantly reduced travel distances at no cost of prediction reliability without any assist of additional modules for shortest path calculation.
‘Uniformity’ or ‘Dispersion’?—The evolution of Chinese poetic word categories’ distribution patterns

Abstract The daily language in mainland China has experienced a shift from traditional Chinese language to modern mandarin Chinese at the beginning of the twentieth century. The Chinese poetry ‘revolution’started in the 1910s is considered as a turning point in the Chinese poetry evolution process due to the novel applications of the modern Chinese language. Many temporal poetic studies consider the poems written in traditional Chinese and modern Chinese as two different genres. The two genres are saliently different in rhyme, meter, theme, etc. We aim to detect the specific properties of the evolution process of Chinese poetry in terms of the word categories’ distribution patterns. For the purpose, a corpus with 438 randomly selected traditional and modern Chinese poems is built, and some quantitative language indicators (entropy, relative entropy, repeat rate) and some exploratory statistical analysis techniques applicable in corpus linguistics and quantitative linguistics (one-way ANOVA test, cluster analysis)1 are used to abstract and analyze language data from the corpus. It is concluded that the word categories are distributed significantly differently in traditional poetry and modern poetry. The sound reasons would be that (1) traditional Chinese poetry is more likely to focus on the application of some specific content word categories, for example, nouns, but not auxiliary words and (2) modern poems tend to choose more categories of words. From the perspective of word class distribution patterns, we suppose that the birth of modern Chinese poetry in the 1910s is a sharp change to Chinese poetry production.
Real-time stereo semi-global matching for video processing using previous incremental information

Abstract This paper presents an incremental stereo algorithm designed to calculate a real-time disparity image. The algorithm is designed for stereo video sequences and uses previous information to reduce computation time and improve disparity image quality. It is based on the semi-global matching stereo algorithm but modified to reuse previous calculation information. Storing and reusing this information not only reduces computation time but improves accuracy in a cost filtering scheme. Some tests are presented to compare the computation time and results of the algorithm, which show that it can achieve better results in terms of quality and time than standard algorithms for some scenarios.
Secure high payload steganography: A model-based approach

The strong energy compaction property of the Discrete Cosine Transform (DCT) has inspired DCT-based steganography techniques to embed secret data in large areas of the insignificant DCT coefficients. Since these coefficients are noncritical, conventional DCT-based steganography techniques replace these insignificant coefficients with the secret data. This basic idea has enabled DCT-based steganography techniques to hide a substantial amount of secret data while maintaining acceptable stego fidelity. However, because these insignificant DCT coefficients do still contribute to the overall quality of the resulting stego image in the spatial domain, there will be an advantage in retaining them as much as possible. Thus, to optimize the stego quality, this paper proposes a novel hiding strategy where the general structure of the insignificant DCT coefficients is retained. A model-based technique is developed using polynomial surface-fitting of the insignificant DCT coefficients, and the secret data is superimposed on the polynomial-modeled coefficients, thus producing a stego image that does not compromise the overall structure of the original cover image. Experimental results demonstrating improvements in the stego quality and security levels of the proposed steganography technique in comparison to state-of-the-art schemes are presented.
Short descriptive answer evaluation using word-embedding techniques

The score given for short answers may vary from instructor to instructor. There are many short answer grading and essay grading systems existing that are either automated or semi-automated. Automated grading systems reduce human effort, but no popular tool still exists. In this paper, we are focusing on short-answer grading systems. We use simple and effective methods to evaluate short descriptive student answers. The similarity between each student's answer with its model answer is evaluated using word embedding algorithms. The similarity score is used to calculate the score. The accuracy of the scores obtained in the case of each algorithm is calculated and analyzed method-wise.
Representing and Validating Cultural Heritage Knowledge Graphs in CIDOC-CRM Ontology

In order to unify access to multiple heterogeneous sources of cultural heritage data, many datasets were mapped to the CIDOC-CRM ontology. CIDOC-CRM provides a formal structure and definitions for most cultural heritage concepts and their relationships. The COURAGE project includes historic data concerning people, organizations, cultural heritage collections, and collection items covering the period between 1950 and 1990. Therefore, CIDOC-CRM seemed the optimal choice for describing COURAGE entities, improving knowledge sharing, and facilitating the COURAGE dataset unification with other datasets. This paper introduces the results of translating the COURAGE dataset to CIDOC-CRM semantically. This mapping was implemented automatically according to predefined mapping rules. Several SPARQL queries were applied to validate the migration process manually. In addition, multiple SHACL shapes were conducted to validate the data and mapping models.
HatRPC

In this paper, we propose a novel hint-accelerated Remote Procedure Call (RPC) framework based on Apache Thrift over Remote Direct Memory Access (RDMA) protocols, called HatRPC. HatRPC proposes a hierarchical hint scheme towards optimizing heterogeneous RPC services and functions. The proposed hint design is composed of service-granularity and function-granularity hints for achieving varied optimization goals and reducing design space for further optimizing the underneath RDMA communication engine. We co-design a key-value store called HatKV with HatRPC and LMDB. The effectiveness and efficiency of HatRPC are validated and evaluated with our proposed Apache Thrift Benchmarks (ATB), YCSB, and TPC-H workloads. Performance evaluations show that the proposed HatRPC approach can deliver up to 55% performance improvement for ATB benchmarks and up to 1.51X speedup for TPC-H queries compared with vanilla Thrift over IPoIB. In addition, the co-designed HatKV can achieve up to 85.5% improvement for YCSB workloads.
A large-scale characterization of online incitements to harassment across platforms

Attack strategies used by online harassers have evolved over time to inflict increasing harm to their targets. In addition to scaling harassment through incitement and coordination, online communities that commonly engage in harassment are likely a source of "innovation" for harassment attack strategies. We use the incitements or calls to harassment posted by members of these communities as a lens through which to holistically measure and understand this ecosystem. We create a filtering pipeline to discover 14,679 incitements to harassment within four large-scale data sets of messages and posts that span multiple platforms.
Boosting anomaly detection using unsupervised diverse test-time augmentation

Anomaly detection is a well-known task that involves the identification of abnormal events that occur relatively infrequently. Methods for improving anomaly detection performance have been widely studied. However, no studies utilizing test-time augmentation (TTA) for anomaly detection in tabular data have been performed. TTA involves aggregating the predictions of several synthetic versions of a given test sample; TTA produces different points of view for a specific test instance and might decrease its prediction bias. We propose the Test-Time Augmentation for anomaly Detection (TTAD) technique, a TTA-based method aimed at improving anomaly detection performance. TTAD augments a test instance based on its nearest neighbors; various methods, including the k-Means centroid and SMOTE methods, are used to produce the augmentations. Our technique utilizes a Siamese network to learn an advanced distance metric when retrieving a test instance’s neighbors. Our experiments show that using our TTA technique significantly improves the performance of anomaly detection algorithms, as evidenced by the higher AUC results achieved on all datasets evaluated. Specifically, we observed average improvements of 0.037 AUC (3.7%) using Autoencoder, 0.016 AUC (1.6%) using OC-SVM, and 0.023 AUC (2.3%) using LOF.
Augmented Tai-Chi Chuan Practice Tool with Pose Evaluation

Tai Chi Chuan (TCC) is a well-known Chinese martial art that promotes health. In addition to learning TCC from a coach in a classroom setting, learners usually use books or videos to practice on their own. However, since turning is a frequent movement in TCC, learners cannot watch a tutorial and practice TCC at the same time. Furthermore, it is difficult for users to determine whether their postures are correct. We propose an augmented reality TCC practice tool with pose evaluation to help people practice TCC on their own. The tool consists of an optical see-through head-mounted display, external cameras, digital compasses, and a server. Users learn TCC movements from surrounding virtual coaches in augmented reality and determine whether their postures are correct via an evaluation module. Study results show that the proposed tool provides a helpful learning environment for TCC and that the pose estimation and evaluation are robust and reliable.
FOX-NAS: Fast, On-device and Explainable Neural Architecture Search

Neural architecture search can discover neural networks with good performance, and One-Shot approaches are prevalent. One-Shot approaches typically require a supernet with weight sharing and predictors that predict the performance of architecture. However, the previous methods take much time to generate performance predictors thus are inefficient. To this end, we propose FOX-NAS that consists of fast and explainable predictors based on simulated annealing and multivariate regression. Our method is quantization-friendly and can be efficiently deployed to the edge. The experiments on different hardware show that FOX-NAS models outperform some other popular neural network architectures. For example, FOX-NAS matches MobileNetV2 and EfficientNet-Lite0 accuracy with 240% and 40% less latency on the edge CPU. Search code and pre-trained models are released at https://github.com/great8nctu/FOX-NAS. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Layer-Wise Relevance Propagation Based Sample Condensation for Kernel Machines

Kernel machines are a powerful class of methods for classification and regression. Making kernel machines fast and scalable to large data, however, is still a challenging problem due to the need of storing and operating on the Gram matrix. In this paper we propose a novel approach to sample condensation for kernel machines, preferably without impairing the classification performance. To our best knowledge, there is no previous work with the same goal reported in the literature. For this purpose we make use of the neural network interpretation of kernel machines. Explainable AI techniques, in particular the Layer-wise Relevance Propagation method, are used to measure the relevance (importance) of training samples. Given this relevance measure, a decremental strategy is proposed for sample condensation. Experimental results on three data sets show that our approach is able to achieve the goal of substantial reduction of the number of training samples.
A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information

Abstract The classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.