Reviewerly/exHarmony huggingface dataset:
Quali-Mat

While sports activity recognition is a well studied subject in mobile, wearable and ubiquitous computing, work to date mostly focuses on recognition and counting of specific exercise types. Quality assessment is a much more difficult problem with significantly less published results. In this work, we present Quali-Mat: a method for evaluating the quality of execution (QoE) in exercises using a smart sports mat that can measure the dynamic pressure profiles during full-body, body-weight exercises. As an example, our system not only recognizes that the user is doing push-ups, but also distinguishes 5 subtly different types of push-ups, each of which (according to sports science literature and professional trainers) has a different effect on different muscle groups. We have investigated various machine learning algorithms targeting the specific type of spatio-temporal data produced by the pressure mat system. We demonstrate that computationally efficient, yet effective Conv3D model outperforms more complex state-of-the-art options such as transfer learning from the image domain. The approach is validated through an experiment designed to cover 47 quantifiable variants of 9 basic exercises with 12 participants. Overall, the model can categorize 9 exercises with 98.6% accuracy / 98.6% F1 score, and 47 QoE variants with 67.3% accuracy / 68.1% F1 score. Through extensive discussions with both the experiment results and practical sports considerations, our approach can be used for not only precisely recognizing the type of exercises, but also quantifying the workout quality of execution on a fine time granularity. We also make the Quali-Mat data set available to the community to encourage further research in the area.
An Efficient LiDAR Point Cloud Map Coding Scheme Based on Segmentation and Frame-Inserting Network

In this article, we present an efficient coding scheme for LiDAR point cloud maps. As a point cloud map consists of numerous single scans spliced together, by recording the time stamp and quaternion matrix of each scan during map building, we cast the point cloud map compression into the point cloud sequence compression problem. The coding architecture includes two techniques: intra-coding and inter-coding. For intra-frames, a segmentation-based intra-prediction technique is developed. For inter-frames, an interpolation-based inter-frame coding network is explored to remove temporal redundancy by generating virtual point clouds based on the decoded frames. We only need to code the difference between the original LiDAR data and the intra/inter-predicted point cloud data. The point cloud map can be reconstructed according to the decoded point cloud sequence and quaternion matrices. Experiments on the KITTI dataset show that the proposed coding scheme can largely eliminate the temporal and spatial redundancies. The point cloud map can be encoded to 1/24 of its original size with 2 mm-level precision. Our algorithm also obtains better coding performance compared with the octree and Google Draco algorithms.
WiAdv

WiFi-based gesture recognition systems have attracted enormous interest owing to the non-intrusive of WiFi signals and the wide adoption of WiFi for communication. Despite boosted performance via integrating advanced deep neural network (DNN) classifiers, there lacks sufficient investigation on their security vulnerabilities, which are rooted in the open nature of the wireless medium and the inherent defects (e.g., adversarial attacks) of classifiers. To fill this gap, we aim to study adversarial attacks to DNN-powered WiFi-based gesture recognition to encourage proper countermeasures. We design WiAdv to construct physically realizable adversarial examples to fool these systems. WiAdv features a signal synthesis scheme to craft adversarial signals with desired motion features based on the fundamental principle of WiFi-based gesture recognition, and a black-box attack scheme to handle the inconsistency between the perturbation space and the input space of the classifier caused by the in-between non-differentiable processing modules. We realize and evaluate our attack strategies against a representative state-of-the-art system, Widar3.0 in realistic settings. The experimental results show that the adversarial wireless signals generated by WiAdv achieve over 70% attack success rate on average, and remain robust and effective across different physical settings. Our attack case study and analysis reveal the vulnerability of WiFi-based gesture recognition systems, and we hope WiAdv could help promote the improvement of the relevant systems.
Query-Efficient Adversarial Attack Based On Latin Hypercube Sampling

In order to be applicable in real-world scenario, Boundary Attacks (BAs) were proposed and ensured one hundred percent attack success rate with only decision information. However, existing BA methods craft adversarial examples by leveraging a simple random sampling (SRS) to estimate the gradient, consuming a large number of model queries. To overcome the drawback of SRS, this paper proposes a Latin Hypercube Sampling based Boundary Attack (LHS-BA) to save query budget. Compared with SRS, LHS has better uniformity under the same limited number of random samples. Therefore, the average on these random samples is closer to the true gradient than that estimated by SRS. Various experiments are conducted on benchmark datasets including MNIST, CIFAR, and ImageNet-1K. Experimental results demonstrate the superiority of the proposed LHS-BA over the state-of-the-art BA methods in terms of query efficiency. The source codes are publicly available at https://github.com/GZHU-DVL/LHS-BA.
Shoes++

Feet are the foundation of our bodies that not only perform locomotion but also participate in intent and emotion expression. Thus, foot gestures are an intuitive and natural form of expression for interpersonal interaction. Recent studies have mostly introduced smart shoes as personal gadgets, while foot gestures used in multi-person foot interactions in social scenarios remain largely unexplored. We present Shoes++, which includes an inertial measurement unit (IMU)-mounted sole and an input vocabulary of social foot-to-foot gestures to support foot-based interaction. The gesture vocabulary is derived and condensed by a set of gestures elicited from a participatory design session with 12 users. We implement a machine learning model in Shoes++ which can recognize two-person and three-person social foot-to-foot gestures with 94.3% and 96.6% accuracies (N=18). In addition, the sole is designed to easily attach to and detach from various daily shoes to support comfortable social foot interaction without taking off the shoes. Based on users' qualitative feedback, we also found that Shoes++ can support team collaboration and enhance emotion expression, thus making social interactions or interpersonal dynamics more engaging in an expanded design space.
A Case Study Investigating a User-Centred and Expert Informed 'Companion Guide' for a Complex Sensor-based Platform

We present a case study that informs the creation of a 'companion guide' providing transparency to potential non-expert users of a ubiquitous machine learning (ML) platform during the initial onboarding. Ubiquitous platforms (e.g., smart home systems, including smart meters and conversational agents) are increasingly commonplace and increasingly apply complex ML methods. Understanding how non-ML experts comprehend these platforms is important in supporting participants in making an informed choice about if and how they adopt these platforms. To aid this decision-making process, we created a companion guide for a home health platform through an iterative user-centred-design process, seeking additional input from platform experts at all stages of the process to ensure the accuracy of explanations. This user-centred and expert informed design process highlights the need to present the platform's entire ecosystem at an appropriate level for those with differing backgrounds to understand, in order to support informed consent and decision making.
Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata

Most statistical agencies release randomly selected samples of Census microdata, usually with sample fractions under 10% and with other forms of statistical disclosure control (SDC) applied. An alternative to SDC is data synthesis, which has been attracting growing interest, yet there is no clear consensus on how to measure the associated utility and disclosure risk of the data. The ability to produce synthetic Census microdata, where the utility and associated risks are clearly understood, could mean that more timely and wider-ranging access to microdata would be possible. This paper follows on from previous work by the authors which mapped synthetic Census data on a risk-utility (R-U) map. The paper presents a framework to measure the utility and disclosure risk of synthetic data by comparing it to samples of the original data of varying sample fractions, thereby identifying the sample fraction which has equivalent utility and risk to the synthetic data. Three commonly used data synthesis packages are compared with some interesting results. Further work is needed in several directions but the methodology looks very promising.
Improving Abusive Language Detection with online interaction network

The rapid development of online social media makes Abusive Language Detection (ALD) a hot topic in the field of affective computing. However, most methods for ALD in social networks do not take into account the interactive relationships among user posts, which simply regard ALD as a task of text context representation learning. To solve this problem, we propose a pipeline approach that considers both the context of a post and the characteristics of interaction network in which it is posted. Specifically, our method is divided into pre-training and downstream tasks. First, to capture fine contextual features of the posts, we use Bidirectional Encoder Representation from Transformers (BERT) as Encoder to generate sentence representations. Later, we build a Relation-Special Network according to the semantic similarity between posts as well as the interaction network structural information. On this basis, we design a Relation-Special Graph Neural Network (RSGNN) to spread effective information in the interaction network and learn the classification of texts. The experiment proves that our method can effectively improve the detection effect of abusive posts over three public datasets. The results demonstrate that injecting interaction network structure into the abusive language detection task can significantly improve the detection results.
Securing color images using DNA coding and cosine stockwell transformation in wavelet domain

In this paper, a color image encoding algorithm is developed using cosine stockwell transformation and DNA coding in wavelet domain. Firstly, a color image is decomposed into its primary color components and then each component is wavelet transformed, which results four subbands for each component. The approximation subband of wavelet transformation is normalized and DNA coding is used followed by the proposed double phase encoding while the detail wavelet subbands are cosine stockwell transformed to get intermediate sub-images. The modified approximation and detail subbands are arranged by their indexing and pseudo random permutation is used to generate an encrypted image. At the receiver end, on having the exact knowledge of all the keys in correct order the original image can be recovered from the encrypted image. The bands of DCST, Tent Sine map is used to generate the random phase mask and key for pseudo random permutation is used as encryption and decryption keys. Numerical analysis and robustness of proposed work is evaluated on encrypted image to show the superiority of proposed work.
Curious instance selection

In the process of building machine learning models data sometimes must be sampled before the learning process can be applied. This step, known as instance selection, is mostly done to reduce the amount of data in a volume that will allow the computing resources required for the learning phase to be reduced. In addition, it also removes noisy data that can affect the learning quality. While the two objectives are often in conflict, in most current approaches, it is impossible to control the balance between them. We propose a reinforcement learning-based approach for instance selection, called curious instance selection (CIS), which evaluates clusters of instances using the curiosity loop architecture. The output of the algorithm is a matrix that represents the value of adding a cluster of instances to existing instances. This matrix enables the computation of the Pareto front and demonstrates the ability to balance the noise and volume reduction objectives. CIS was evaluated on five datasets, and its performance was compared with the performance of three state-of-the-art algorithms. Our results show that CIS not only provides enhanced flexibility but also achieves higher effectiveness (reduction times accuracy). This approach strengthens the appeal of using curiosity-based algorithms in data science.
An efficient hyper-parameter optimization method for supervised learning

Supervised learning is an important tool for data mining and knowledge discovery. The hyper-parameter in learning models usually has a significant impact on the generalization performance of supervised learning model. Although some state-of-the-art hyper-parameter optimizers, such as cross-validation (CV) and its improvements, have been widely used in many applications, there still exist some limitations including the low efficiency and the error variation from data partition. To address these issues, we propose the sign similarity to distinguish between over-fitting and under-fitting in supervised learning. On this basis, the minimal symmetric similarity criterion (MSSC) is proposed to optimize hyper-parameters. It provides an equivalent condition of well-fitting, i.e., the well-fitted hyper-parameter should have a minimal symmetric similarity. Compared with CV, the criterion is more efficient and could avoid error variation as the symmetric similarity is based on the whole data set without partition. The reasonableness of the proposed MSSC is proved for well-posed learning problems. Also, corresponding assumptions are explained by means of theoretical analysis and empirical verification. Both theoretical analysis and experimental results indicate that the efficiency of MSSC is significantly higher than popular optimizers like 10-CV. Experimental results demonstrate that MSSC is comparable with or outperforms the state-of-the-art optimizers in three kinds of learning tasks from the statistical perspective.
Modified layer deep convolution neural network for text-independent speaker recognition

Speaker recognition is the task of identifying the spokesman automatically using speaker-specific features. It has been a popular and most involved topic in the field of speech technology. This field opens a wide opportunity for research and finds its application in the areas such as forensics, authentication, security, etc. In this work, a modified deep-convolutional neural network structure has been proposed for speaker identification that has improved convolution, activation, and pooling layers along with Adam’s optimiser. The proposed architecture yielded the increase of prediction accuracy and reduction of Loss function when compared to the generic Convolutional Neural Network scheme. The execution of the proposed architecture is validated by various datasets and the outcomes show that the modified CNN performs better than the other state-of-the-art models regarding both accuracy (avg 99%) and loss function (avg 1%). From the analysis, it is found that the Modified-CNN suits the best for real-time speaker identification applications as the efficacy of the model does not degrade due to the effects of noise and interferences that are caused in the recording environment. Relevance of the work: Speaker Recognition is an area of interest in which ML and DL schemes, when combined, have the potential to make history in the areas of Automation and Authentication. Using a modified CNN can enhance the process by ignoring many issues such as false positives, background noise, and so on. This process can be expanded to create a Raga Identification and Therapy mechanism that can be used to treat diseases.
Transaction Monitoring of Smart Contracts

Blockchains are modern distributed systems that provide decentralized financial capabilities with trustable guarantees. Smart contracts are programs written in specialized programming languages running on a blockchain and govern how tokens and cryptocurrency are sent and received. Smart contracts can invoke other contracts during the execution of transactions initiated by external users. Once deployed, smart contracts cannot be modified and their pitfalls can cause malfunctions and losses, for example by attacks from malicious users. Runtime verification is a very appealing technique to improve the reliability of smart contracts. One approach consists of specifying undesired executions (never claims) and detecting violations of the specification on the fly. This can be done by extending smart contracts with additional instructions corresponding to monitor specified properties, resulting in an onchain monitoring approach. In this paper, we study transaction monitoring that consists of detecting violations of complete transaction executions and not of individual operations within transactions. Our main contributions are to show that transaction monitoring is not possible in most blockchains and propose different execution mechanisms that would enable transaction monitoring.
Resources in translation: towards a conceptual and technical apparatus

Abstract In mathematics education, translation of resources from one language to another occurs in a wide range of situations. This paper explores how conceptual and technical apparatus from contemporary translation studies may be of use in guiding and analysing such translation. Key concepts—including those of source, target and intermediate text, of paradigms of equivalence, purpose, uncertainty and localisation, and of semantic, syntactic and epistemological equivalence—are introduced and illustrated, primarily with reference to translation of a series of school mathematics workbooks. Significant types of tool to assist translation—translation protocols and machine translation—are examined. A more detailed case study illustrates techniques (and associated tools) for analysing translation shifts and terminological translatability, applying them to examine translation of documentation of a framework for researching teachers’ interaction with resources—the Documentational Approach to Didactics. Specific insights that emerge are how translation is shaped by attention not just to fidelity to the source text but to the audience for, and function of, the target text; and how challenging it can be to formulate terms translatable across languages in such a way as to consistently anchor meaning in existing wordforms.
Lexi <sup>2</sup>

Bloat, a well-known phenomenon in Evolutionary Computation, often slows down evolution and complicates the task of interpreting the results. We propose Lexi2, a new selection and bloat-control method, which extends the popular lexicase selection method, by including a tie-breaking step which considers attributes related to the size of the individuals. This new step applies lexicographic parsimony pressure during the selection process and is able to reduce the number of random choices performed by lexicase selection (which happen when more than a single individual correctly solve the selected training cases).
Performance analysis of distance metrics on the exploitation properties and convergence behaviour of the conventional firefly algorithm

The nature of the distance metric used to compare the span between two fireflies in conventional firefly algorithm can significantly enhance the exploitation properties of the algorithm and improve its convergence towards the optimum solution. Therefore, this research proposes several variants of the firefly algorithm based on different distance relations like Minkowski distance, Canberra distance, and Angular Cosine distance. To compare the performance of these variants, benchmark non-linear and non-convex objective functions are solved under the varying nature of the medium absorption coefficient and attractiveness constant. It is observed from detail analysis of results that Canberra distance provides optimal results for almost all of the benchmark functions. Furthermore, the importance of the distance metric for real-world optimization problems with very large or small magnitude of decision variables is also presented by solving a practical hydro thermal scheduling problem. It is also presented that the optimal set of parameters for a particular distance criterion may not always be optimal and better convergence results can be achieved by varying the distance metric to measure the length between the fireflies.
Recruiting credible participants for field studies in software engineering research

Software practitioners are a primary provider of information for field studies in software engineering. Research typically recruits practitioners through some kind of sampling. But sampling may not in itself recruit the “right” participants. To assess existing guidance on participant recruitment, and to propose and illustrate a framework for recruiting professional practitioners as credible participants in field studies of software engineering. We review existing guidelines, checklists and other advisory sources on recruiting participants for field studies. We develop a framework, partly based on our prior research and on the research of others. We search for and select three exemplar studies (a case study, an interview study and a survey study) and use those to illustrate the framework. Whilst existing guidance recognises the importance of recruiting participants, there is limited guidance on how to recruit the “right” participants. The framework suggests the conceptualisation of participants as “research instruments” or, alternatively, as a sampling frame for items of interest. The exemplars suggest that at least some members of the research community are aware of the need to carefully recruit the “right” participants. The framework is intended to encourage researchers to think differently about the involvement of practitioners in field studies of software engineering. Also, the framework identifies a number of characteristics not explicitly addressed by existing guidelines.
Consensus algorithm based on verifiable randomness

Consensus algorithms are the core of blockchain technology, which can cause nodes to reach consistency or liveness when there are Byzantine nodes in the network. The generation of public randomness in decentralized networks has been significantly demanding and challenging in terms of the consensus mechanism. Previously, the multi-party random number generator (mRNG), which is a mechanism for creating a single value from the contributions of decentralized multiple parties, was mainly designed based on the verifiable random function. In this study, we first construct novel, efficient verifiable mRNG protocols from any one-way function. The protocols can achieve the properties of fairness, no trusted third party, public verifiability, and manipulation resistance. Subsequently, we propose a delegated PoS (DPoS)-based consensus algorithm that adopts the verifiable mRNG. The new algorithm can solve the problem of low fairness caused by the artificial election of master nodes using DPoS, while addressing the issue of manipulating the consensus process owing to the pseudo-random number generated by the traditional RNG, thereby improving the credibility of the consensus algorithm.