Reviewerly/exHarmony huggingface dataset:
NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access

Parameter servers (PSs) facilitate the implementation of distributed training for large machine learning tasks. In this paper, we argue that existing PSs are inefficient for tasks that exhibit non-uniform parameter access; their performance may even fall behind that of single node baselines. We identify two major sources of such non-uniform access: skew and sampling. Existing PSs are ill-suited for managing skew because they uniformly apply the same parameter management technique to all parameters. They are inefficient for sampling because the PS is oblivious to the associated randomized accesses and cannot exploit locality. To overcome these performance limitations, we introduce NuPS, a novel PS architecture that (i) integrates multiple management techniques and employs a suitable technique for each parameter and (ii) supports sampling directly via suitable sampling primitives and sampling schemes that allow for a controlled quality--efficiency trade-off. In our experimental study, NuPS outperformed existing PSs by up to one order of magnitude and provided up to linear scalability across multiple machine learning tasks.
Construction of Conceptual Prospecting Model Based on Geological Big Data: A Case Study in Songtao-Huayuan Area, Hunan Province

With the era of big data, the prediction and evaluation of geological mineral resources have gradually entered into a new stage from digital prospecting to intelligent prospecting. The theoretical method of big data mining can contribute to deep mineral resource prediction and evaluation. This paper extracts ore-causing and ore-caused anomaly information based on text intelligent mining technology, and constructs a regional conceptual prospecting model based on geological prospecting big data. First, we set up a corpus based on text big data discovery and preprocessing technology. Second, we used CNN multiple scale text classification technology to analyze geological text data from the two main aspects: ore-causing anomalies and ore-caused anomalies. Third, we used a statistical method to analyze the semantic links between content-words, and we constructed chord diagrams and ternary diagrams to visualize the content-words and their links. Finally, we constructed a regional conceptual prospecting model based on the knowledge graphs.
Tamp-X: Attacking explainable natural language classifiers through tampered activations

While the technique of Deep Neural Networks (DNNs) has been instrumental in achieving state-of-the-art results for various Natural Language Processing (NLP) tasks, recent works have shown that the decisions made by DNNs cannot always be trusted. Recently Explainable Artificial Intelligence (XAI) methods have been proposed as a method for increasing DNN’s reliability and trustworthiness. These XAI methods are however open to attack and can be manipulated in both white-box (gradient-based) and black-box (perturbation-based) scenarios. Exploring novel techniques to attack and robustify these XAI methods is crucial to fully understand these vulnerabilities. In this work, we propose Tamp-X—a novel attack which tampers the activations of robust NLP classifiers forcing the state-of-the-art white-box and black-box XAI methods to generate misrepresented explanations. To the best of our knowledge, in current NLP literature, we are the first to attack both the white-box and the black-box XAI methods simultaneously. We quantify the reliability of explanations based on three different metrics—the descriptive accuracy, the cosine similarity, and the Lp norms of the explanation vectors. Through extensive experimentation, we show that the explanations generated for the tampered classifiers are not reliable, and significantly disagree with those generated for the untampered classifiers despite that the output decisions of tampered and untampered classifiers are almost always the same. Additionally, we study the adversarial robustness of the tampered NLP classifiers, and find out that the tampered classifiers which are harder to explain for the XAI methods, are also harder to attack by the adversarial attackers.
Nuclei-Guided Network for Breast Cancer Grading in HE-Stained Pathological Images

Breast cancer grading methods based on hematoxylin-eosin (HE) stained pathological images can be summarized into two categories. The first category is to directly extract the pathological image features for breast cancer grading. However, unlike the coarse-grained problem of breast cancer classification, breast cancer grading is a fine-grained classification problem, so general methods cannot achieve satisfactory results. The second category is to apply the three evaluation criteria of the Nottingham Grading System (NGS) separately, and then integrate the results of the three criteria to obtain the final grading result. However, NGS is only a semiquantitative evaluation method, and there may be far more image features related to breast cancer grading. In this paper, we proposed a Nuclei-Guided Network (NGNet) for breast invasive ductal carcinoma (IDC) grading in pathological images. The proposed nuclei-guided attention module plays the role of nucleus attention, so as to learn more nuclei-related feature representations for breast IDC grading. In addition, the proposed nuclei-guided fusion module in the fusion process of different branches can further enable the network to focus on learning nuclei-related features. Overall, under the guidance of nuclei-related features, the entire NGNet can learn more fine-grained features for breast IDC grading. The experimental results show that the performance of the proposed method is better than that of state-of-the-art method. In addition, we released a well-labeled dataset with 3644 pathological images for breast IDC grading. This dataset is currently the largest publicly available breast IDC grading dataset and can serve as a benchmark to facilitate a broader study of breast IDC grading.
Tiny-MLOps: a framework for orchestrating ML applications at the far edge of IoT systems

Empowering the Internet of Things devices with Artificial Intelligence capabilities can transform all vertical applications domains within the next few years. Current approaches favor hosting Machine Learning (ML) models on Linux-based single-board computers. Nevertheless, these devices’ cost and energy requirements limit the possible application scenarios. Conversely, today’s available 32-bit microcontrollers have much lower costs and only need a few milliwatts to operate, making them an energy-efficient and cost-effective alternative. However, the latter devices, usually referred to as far edge devices, have stringent resource constraints and host non-Linux-based embedded real-time operating systems. Therefore, orchestrating such devices executing portions of ML applications represents a major challenge with current tools and frameworks. This paper formally introduces the Tiny-MLOps framework as the specialization of standard ML orchestration practices, including far edge devices in the loop. To this aim, we will tailor each phase of the classical ML orchestration loop to the reduced resources available onboard typical IoT devices. We will rely on the proposed framework to deliver adaptation and evolving capabilities to resource-constrained IoT sensors mounted on an industrial rotary machine to detect anomalies. As a feasibility study, We will show how to programmatically re-deploy ML-based anomaly detection models to far edge devices. Our preliminary experiments measuring the system performance in terms of deployment, loading, and inference latency of the ML models will corroborate the usefulness of our proposal.
Breast Calcifications and Histopathological Analysis on Tumour Detection by CNN

The most salient argument that needs to be addressed universally is Early Breast Cancer Detection (EBCD), which helps people live longer lives.The Computer-Aided Detection (CADs)/Computer-Aided Diagnosis (CADx) system is indeed a software automation tool developed to assist the health professions in Breast Cancer Detection and Diagnosis (BCDD) and minimise mortality by the use of medical histopathological image classification in much less time.This paper purposes of examining the accuracy of the Convolutional Neural Network (CNN), which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early identification of breast cell malignancies formation of masses and Breast microcalcifications on the mammogram.When we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network (ResNet50) for Breast Cancer Detection and Diagnosis, to obtain the Discriminative Localization, Convolutional Neural Network with Class Activation Map (CAM) has also been used to perform breast microcalcifications detection to find a specific class in the Histopathological image.The test results indicate that this method performed almost 225.15%better at determining the exact location of disease (Discriminative Localization) through breast microcalcifications images.ResNet50 seems to have the highest level of accuracy for images of Benign Tumour (BT)/Malignant Tumour (MT) cases at 97.11%.ResNet50's average accuracy for pre-trained Convolutional Neural Network is 94.17%.
An Exploratory Study on the Design and Management Model of Traditional Chinese Medicine Quality Safety Traceability System Based on Blockchain Technology

Aiming at the problems of long life cycle complex roles of participants, diverse risk factors, poor supervision coverage and difficult information traceability of TCM (Traditional Chinese Medicine) supply chain, this paper constructs a TCM quality safety traceability system based on blockchain technology by analyzing the business process and supervision characteristics of TCM supply chain. Blockchain technology is a new application mode of computer technology such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, and other computer technologies. It has the characteristics of “decentralization, nontampering, transparent and open, and data traceability.” The function of nontampering, hash function, and time stamp can effectively solve the traceability problem of TCM supply chain. Through the analysis of the blockchain TCM traceability principle and network structure, combined with the actual business process of TCM traceability, this paper designs the blockchain TCM quality safety traceability system, analyzes its overall structure, data storage mode, digital signature, consensus algorithm, and main functions, and preliminarily constructs a new model of TCM quality safety traceability management based on blockchain technology. Using blockchain technology to realize the quality safety traceability of TCM, we can complete the omnidirectional, multiangle, and wide coverage of the data and information in whole supply chain of TCM breeding and seedling raising, planting, innovation and research and development, processing of TCM pieces, circulation of TCM, and consumption, so as to realize that the main body of responsibility of TCM can be put on record, the production records can be queried, product flow can be traced, quality safety can be forewarned, main responsibility can be identified, regulatory information can be shared, and product source can be traced.
CoCycleReg: Collaborative cycle-consistency method for multi-modal medical image registration

Multi-modal image registration is an essential step for many medical image analysis applications. Recent advances in multi-modal image registration rely on image-to-image translation to achieve good performance. However, the performance is still limited owing to the poor use of complementary regularization between image registration and translation, which is able to simultaneously enhance both parts’ accuracy. To this end, we propose CoCycleReg, a novel method that formulates image registration and translation in a Collaborative Cycle-consistency manner. Instead of dividing into two discrete stages, we unify the image registration and translation via cycle-consistency in an end-to-end training process, such that each part can benefit from the other one. To ensure the deformation fields’ reversibility in the cycle, we extensively introduce a novel dual-head registration network, consisting of one single backbone to extract the features and two heads to respectively predict the deformation fields. The experiments on T1-T2(MRI) and CT-MRI datasets validate that the proposed CoCycleReg surpasses the other state-of-the-art conventional and deep learning approaches comprehensively considering the speed, accuracy, and regularity of deformation fields. In the ablation analysis, a method that sets the cycle-consistency Corresponding authors at: Department of Computer Science at School of Informatics, Xiamen University, Xiamen 361005, Chinaconstraints of registration and image-to-image translation separately is compared, and the results demonstrate the effectiveness of collaborative cycle-consistency. In addition, the improvement of image-to-image translation is also verified in further analysis. The code is publicly available at https://github.com/DopamineLcy/cocycle-reg/.
A Survey on Network Optimization Techniques for Blockchain Systems

The increase of the Internet of Things (IoT) calls for secure solutions for industrial applications. The security of IoT can be potentially improved by blockchain. However, blockchain technology suffers scalability issues which hinders integration with IoT. Solutions to blockchain’s scalability issues, such as minimizing the computational complexity of consensus algorithms or blockchain storage requirements, have received attention. However, to realize the full potential of blockchain in IoT, the inefficiencies of its inter-peer communication must also be addressed. For example, blockchain uses a flooding technique to share blocks, resulting in duplicates and inefficient bandwidth usage. Moreover, blockchain peers use a random neighbor selection (RNS) technique to decide on other peers with whom to exchange blockchain data. As a result, the peer-to-peer (P2P) topology formation limits the effective achievable throughput. This paper provides a survey on the state-of-the-art network structures and communication mechanisms used in blockchain and establishes the need for network-based optimization. Additionally, it discusses the blockchain architecture and its layers categorizes existing literature into the layers and provides a survey on the state-of-the-art optimization frameworks, analyzing their effectiveness and ability to scale. Finally, this paper presents recommendations for future work.
Achieving "A's for All (as Time and Interest Allow)"

The SIGCSE-MEMBERS mailing list of the ACM Special Interest Group in Computer Science Education is the main forum for educators worldwide to discuss computing education research, pedagogy, and curriculum. In early 2022 it was abuzz with several connected movements: growth mindset, proficiency (aka mastery) learning, grading for equity, and specifications grading. Each of these is an important step toward the Holy Grail: A's for All (as time and interest allow); the "A" line doesn't move, but every student should be given an opportunity to achieve proficiency and earn it, as long as they are willing to put in the time and effort it might take. The mantra is not "fixed time, variable learning", but "fixed learning, variable time". The goal of this full-day workshop is to provide educators and administrators with tools to achieve it in their courses and institutions.
Improved A-star algorithm based on multivariate fusion heuristic function for autonomous driving path planning

Path planning is a fundamental problem in the aspect of autonomous driving. A-star (A*) algorithm is a heuristic algorithm for path planning. However, there are two problems need to be solved in the traditional A-star algorithm: firstly, the tracking error caused by vehicle speed and vehicle size are not considered in path planning; secondly, the kinematics constraints of the vehicle itself and the smoothness of the path in the actual driving process are not considered. Therefore, this paper proposes an improved A-star algorithm to address the above deficiencies. By designing the collision cost heuristic function based on the position of obstacles, the vehicle contour and speed are considered and the vehicle safety is improved by setting a safe space around the vehicle, and establishing a steering cost heuristic function based on the control of heading angle difference, the vehicle dynamics constraints are satisfied, so as to improve the feasibility of path planning. The experimental results show that the improved A-star algorithm can avoid vehicle contours collision at different speeds and output a smoother path, and effectively generate higher quality path.
Addressing as a gender-preferential way for suggestive selling in Chinese e-commerce live streaming discourse: A corpus-based approach

This paper explores sellers' addressing behaviours in Chinese e-commerce live streaming discourse by focusing on the impact of gender. Data were drawn from a self-built corpus comprising 120 h of transcribed texts from live streaming on Taobao. By conducting a corpus-based analysis, we compared males' and females' keywords for addressing customers and their performative functions of suggestive selling. The findings show that: (1) both males and females use a small percentage of kinship terms; (2) males' ratio of address pronouns to nouns greatly surpasses females', but their frequency of ‘nín’ (deferential you) is much lower than females'; (3) females are more likely to consistently use nouns with the meaning of baby, whereas males prefer utilising diverse friendship terms; and (4) females frequently use a pattern of ‘thank/greet + baby’ to conduct ritual interactions more often than males. We argue that both males' and females' addressing behaviours function to promote successful selling; however, gender distinctions do exist in terms of their lexical choices and pragmatic uses of addressing words. The current study contributes to both pragmatics and the sociolinguistic study of gender and language.
Assessing website password practices – Unchanged after fifteen years?

Passwords continue to occupy an interesting position in cyber security, being both widely used and widely criticised at the same time. In many cases the criticism is levelled at users, who are routinely judged to be at fault for making weak choices. However, such judgements frequently tend to overlook that fact that users were ultimately permitted to make such choices, and often guided to do no better. This paper presents the fifth in a series of studies that have been conducted every 3-4 years since 2007 and examines the extent to which leading websites guide and support users in making appropriate password choices. Following the same core approach as the prior investigations, it examines the password practices of ten leading websites, with the aim of examining the level of guidance provided to users before and during the password selection process, as well as examining the nature of the passwords that users are then permitted to choose. The findings reveal that while there have been some marginal improvements in some areas (e.g. for the first time all of the sites under test were found to enforce a minimum password length), there are still numerous shortcomings and omissions in areas that would arguably support users in improving both their password practices and their resultant protection. Most sites present no upfront information on what good passwords should look like, and many offer ambiguous feedback in response to choices that are not permitted. Moreover, when it comes to filtering and preventing the use of weak passwords, there are a range of surprising omissions, with some sites readily permitting common passwords that others block for being too obvious. The findings demonstrate that users can remain unguided on good practice and unchallenged on bad decisions, which continues to be a disappointing outcome in an area of cyber security that is used so often.
Interpretable Data-Based Explanations for Fairness Debugging

A wide variety of fairness metrics and eXplainable Artificial Intelligence (XAI) approaches have been proposed in the literature to identify bias in machine learning models that are used in critical real-life contexts. However, merely reporting on a model's bias or generating explanations using existing XAI techniques is insufficient to locate and eventually mitigate sources of bias. We introduce Gopher, a system that produces compact, interpretable, and causal explanations for bias or unexpected model behavior by identifying coherent subsets of the training data that are root-causes for this behavior. Specifically, we introduce the concept of causal responsibility that quantifies the extent to which intervening on training data by removing or updating subsets of it can resolve the bias. Building on this concept, we develop an efficient approach for generating the top-k patterns that explain model bias by utilizing techniques from the machine learning (ML) community to approximate causal responsibility, and using pruning rules to manage the large search space for patterns. Our experimental evaluation demonstrates the effectiveness of Gopher in generating interpretable explanations for identifying and debugging sources of bias.
TEX(alive): A TOOLKIT TO EXPLORE TEMPORAL EXPRESSIONS IN SHAPE-CHANGING TEXTILE INTERFACES

Shape-changing textile interfaces have the potential to create unique functions, expressions, and interactions in everyday artifacts. However, the technical expertise required to fabricate and interact with these interfaces limits designers from rapidly iterating through diverse textile expressions. This pictorial presents TEX(alive), a low-cost and open-source physical-digital toolkit to facilitate the creation of temporal expressions in textile interfaces. TEX(alive) comprises pneumatic actuators that can be interactively configured across a 3d printed grid structure on the textile. Creative sessions with seven designers show that TEX(alive) supports the exploration of temporality in textile interfaces, opening up a design space for unforeseen future application scenarios and alive-like expressions in material-driven design. Finally, we suggest coupling TEX(alive) with a computational simulation tool to allow designers to predict spatial shape change when the textile interface increases in size or complexity.