Reviewerly/exHarmony huggingface dataset:
Stochastic recognition of human daily activities via hybrid descriptors and random forest using wearable sensors

Human daily activity recognition (HDAR) using wearable sensors is an important task for researchers aiming to develop an effective and feasible model which is capable of accurately detecting human motion patterns. These applications provide elderly care, surveillance systems, and wellness tracking. Despite the pervasive use, recognition and monitoring of human physical activities remains inaccurate, which may contribute to negative reactions and feedback. This paper addresses a data-driven approach to recognizing human daily activities in an indoor-outdoor environment. To improve the classification and recognition of human life-log activities (for example, walking, drinking, and exercising), a model is introduced that incorporates pre-processing (such as denoising), hybrid features extraction from four domains, including time, frequency, wavelet, and time-frequency respectively. After that, stochastic gradient descent is exploited to optimize the selected features. The optimal extracted features are advanced to random forest classifiers in order to develop adaptive for human life-log activities. Additionally, the proposed HDAR model is experimentally evaluated on three benchmark datasets, namely, USC-HAD, which is comprised of 12 physical activities, IM-WSHA, which involves 11 life-log activities, and MOTIONSENSE which contains six static and dynamic activities, respectively. The experimental results show that the proposed HDAR method significantly achieves better results and outperforms others in terms of recognition rates of 91.08%, 91.45%, and 93.16% respectively, when the USC-HAD, IM-WSHA, and MOTIONSENE databases are applied.
A classification for complex imbalanced data in disease screening and early diagnosis

Imbalanced classification has drawn considerable attention in the statistics and machine learning literature. Typically, traditional classification methods often perform poorly when a severely skewed class distribution is observed, not to mention under a high-dimensional longitudinal data structure. Given the ubiquity of big data in modern health research, it is expected that imbalanced classification in disease diagnosis may encounter an additional level of difficulty that is imposed by such a complex data structure. In this article, we propose a nonparametric classification approach for imbalanced data in longitudinal and high-dimensional settings. Technically, the functional principal component analysis is first applied for feature extraction under the longitudinal structure. The univariate exponential loss function coupled with group LASSO penalty is then adopted into the classification procedure in high-dimensional settings. Along with a good improvement in imbalanced classification, our approach provides a meaningful feature selection for interpretation while enjoying a remarkably lower computational complexity. The proposed method is illustrated on the real data application of Alzheimer's disease early detection and its empirical performance in finite sample size is extensively evaluated by simulations.
Aggregating and Thresholdizing Hash-based Signatures using STARKs

This work presents an approach for compressing hash-based signatures using STARKs (Ben-Sasson et. al.'18). We focus on constructing a hash-based t-of-n threshold signature scheme, as well as an aggregate signature scheme. In both constructions, an aggregator collects individual one-time hash-based signatures and outputs a STARK proof attesting that the signatures are valid and meet the required thresholds. This proof then serves the role of the aggregate or threshold signature. We demonstrate the concrete performance of such constructions, having implemented the algebraic intermediate representations (AIR) for them, along with an experimental evaluation over our implementation of the STARK protocol.
Clinical short text classification method based on ALBERT and GAT

As one of the key tasks in natural language processing, the accuracy of short text classification will directly affect the performance of subsequent downstream tasks. Most of the existing short text classification algorithms usually do not understand the deep semantic information related to the medical field and lack the feature extraction of medical proprietary vocabulary. Traditional models often extract features from context information, which is limited by the lack of access to global and deep semantic information. This paper presents a short text classification algorithm for Chinese clinical medicine combining ALBERT pre-training model and graph attention network. The ALBERT pre-training model is used to represent the feature vectors of the short text of the problem, and then the graphical structure data is generated from the feature vectors. GAT is used to assign different weights to each node in a uniform neighborhood. Finally, a map-level semantic representation for category prediction is generated. The experimental results show that the accuracy of the model is 83.27% on Chinese clinical problem datasets, which effectively improves the model performance.
Automated Scoring for Reading Comprehension via In-context BERT Tuning

Automated scoring of open-ended student responses has the potential to significantly reduce human grader effort. Recent advances in automated scoring leverage textual representations from pre-trained language models like BERT. Existing approaches train a separate model for each item/question, suitable for scenarios like essay scoring where items can be different from one another. However, these approaches have two limitations: 1) they fail to leverage item linkage for scenarios such as reading comprehension where multiple items may share a reading passage; 2) they are not scalable since storing one model per item is difficult with large language models. We report our (grand prize-winning) solution to the National Assessment of Education Progress (NAEP) automated scoring challenge for reading comprehension. Our approach, in-context BERT fine-tuning, produces a single shared scoring model for all items with a carefully designed input structure to provide contextual information on each item. Our experiments demonstrate the effectiveness of our approach which outperforms existing methods. We also perform a qualitative analysis and discuss the limitations of our approach. (Full version of the paper can be found at: https://arxiv.org/abs/2205.09864 Our implementation can be found at: https://github.com/ni9elf/automated-scoring )
Speedster

State channel network is the most popular layer-2 solution to the issues of scalability, high transaction fees, and low transaction throughput of public Blockchain networks. However, the existing works have limitations that curb the wide adoption of the technology, such as the expensive creation and closure of channels, strict synchronization between the main chain and off-chain channels, frozen deposits, and inability to execute multi-party smart contracts. In this work, we present Speedster, an account-based state-channel system that aims to address the above issues. To this end, Speedster leverages the latest development of secure hardware to create dispute-free certified channels that can be operated efficiently off the Blockchain. Speedster is peer-to-peer decentralized and provides better privacy protection than prior channel projects. It supports fast native multi-party contract execution, which is previously unavailable in TEE-enabled channel networks. Compared to the Lightning Network, Speedster improves the throughput by about 10,000X and generates 97%$ less on-chain data with a comparable network scale.
Distracted Driving Detection by Combining ViT and CNN

The risk of road accidents is rising rapidly. Distracted driving remains one of the leading causes of traffic accidents. Therefore, the identifying of the distracted driving become significant. Extensive methods based on the convolutional neural network (CNN) have been applied to the detection of the distracted driving. Within Convolutional Neural Network (CNN), the convolution operations are good at extracting local features but experience difficulty to capture global representations. Within visual transformer (ViT), the cascaded self-attention modules perform surpassingly in capturing content-based global interactions but unfortunately deteriorate local feature details. In order to address those challenges mentioned before, we propose a new distracted driving detection method that utilizes the driver and related object cues as guidance and combines CNN with ViT as a backbone to capture the local and global features. Besides, the simulation module is introduced to obtain the result of classification during a certain time period in the stage of inference. Under the widely used StateFarm benchmark, our proposed method presents the best performance.
Collaborative Prediction of Examinee Performance based on Fuzzy Cognitive Diagnosis via cloud model

The prediction of examinee performance via cognitive diagnosis models might provide an important decision-making support for personalized learning instruction in an e-learning system. Aiming at the uncertainty of learners' skill proficiency caused by the complexity of skills, and the large-scale volume of score profiles, a collaborative prediction approach of examinee performance is proposed based on a new fuzzy cloud cognitive diagnosis model. In this approach, the normal cloud models are used to measure the uncertainty of the skill proficiency from three aspects (i.e., expectation, variation degree, and variation frequency), and an e-learner’s skill proficiency is characterized with a fuzzy interval number. Based on a collaborative parameter estimation method, the predicted scores on every test item could be obtained for learners. Finally, the experiments demonstrate that this approach provides good accuracy and less execution time for predicting examinee performance than other approaches.
Sentiment Analysis on Online Reviews using Machine Learning and NLTK

Advancement in technology paved the way to the new era where machines play great role. Machines make decisions and understand us based on our behaviors and can be trained by data to learn and provide a good accuracy in analyzing business data insights, recommending of goods. The sentiment analysis determines the product effectiveness felt to the customers or public in terms of ratings. The analysis is carried out on modern data science tool and technologies. The machine learning and natural language processing tools and algorithms determines the sentiment classifications either in positive, negative, or neutral class. The accusation of data, through preprocessing and feature extraction in supervised form has been analyzed for the accuracy enhancement on a particular set of algorithms and NLTK tools. The pre architecture was designed and the data sets were then implemented for the effective results and accuracy. In this paper, different machine learning algorithms are applied with natural language kit tools for extraction of features, and it is concluded that logistic regression works best with accuracy of 80% and rest algorithms have less accuracy as compared to logistic regression.
Smart Attendance Marking System using Face Recognition

This paper deals with facial based recognition attendance management. Face recognition is becoming one of the most widely used technologies and important features of computer vision. It identifies a person spontaneously from a still snap or reel frame. It is used in monitoring the attendance of students and staff in an educational organisation since it is very important to manage the attendance of the students. There are a few issues springing up whilst reflecting on the consideration of the conventional technique for taking attendance in a magnificence room, which consumes a whole lot of time to take the attendance and additionally it's far tough to maintain. So, the automated attendance control machine is proposed. In this project, the attendance for scholars marked the use of face detection and face reputation through open laptop imaginative and prescient without human interference. A concept of switching from conventional manner of attendance to virtual face reputation and detection strategies with the aid of the use of gadget mastering algorithms has been discussed. The proposed approach routinely detects the scholar whilst he enters withinside the magnificence room and marks the attendance with the aid of spotting him. For this, a Dataset of individualities for discovery, recognition and attendance marking has been created. This concept is completely grounded on preferred motive language, via which the idea of open laptop vision is used. According to the proposed method, the digital digicam is constant within the study room, and it'll seize the photograph, then the faces are detected after which it's far identified with the database and in the end the attendance is marked. If the attendance is marked as on leave, the message about the students on leave can be dispatched to their parents. There are many techniques for evaluating faces. Here, haar cascade is used for face detection and LBPH version for face recognition. Moreover, the schooling of person students is executed and in the long run the gadget generates the spreadsheet which presents attendance of a pupil's present/ absent with inside the study room with a photograph or video taking pictures live. While evaluating the everyday attendance marking with the proposed gadget, this approach saves the time and additionally allows covering the students.
Speaking System for Deaf and Mute People with Flex Sensors

Recent advancements in technology have provided solutions to the communication of deaf and mute individuals. In this paper, the main objective is to provide easiness of communication and to implement an automatic speaking system for deaf and mute people. Communication is one of the most vital behaviors of a human. Every living being has its way of communication. What would it be like if humans could not talk or listen! The primary purpose of this project is to help people who cannot speak or hear. Since many of us cannot understand sign language, it will help people with disabilities to communicate with us and enable them to share everything. It will play an essential role in healthcare and education. This research study utilizes wearable gloves so that anyone can afford this easily. Four flex sensors, an accelerometer, a speaker module, gloves, an Arduino Uno, an LCD screen, a GSM 800L module, and lithium-ion rechargeable battery are used to develop an intelligent sign language recognition system for dumb people. Three rechargeable batteries were used to charge Arduino Uno, GSM, and speaker module. The sensor converts the detected execution load into a voltage and the Arduino Uno microcontroller detects this voltage. Finally, the detected signs are displayed on the LCD to display and pronounced verbally by the speaker.
Development of Image Processing Based on Deep Learning Algorithm

With the rapid development of computer technology and information technology, the development of deep learning has been greatly promoted, and as the mainstream trend of the development of deep learning, there is a great technological breakthrough in the field of image processing. This paper mainly focuses on the development of image processing technology supported by deep learning algorithm, using particle swarm algorithms, image matching algorithms and deletion strategies to optimize image processing technology, and it is found that each of these methods plays a role in pattern recognition, obtaining deeper meaning of images and deleting unimportant information. Deep learning algorithm enable the processing of a large amount of stored information and ensure the integrity of the image in the process of optimizing image processing.
Vulnerable Smart Contract Detection by Means of Model Checking

We are recently witnessing an exponential use of blockchain technology since information infrastructures are increasingly moving from the concept of centralisation to the decentralization ones. Blockchain technology is strictly related to the exchange of sensitive information, and its massive adoption is corresponding to interest from attackers. In this paper, we propose a method to automatically detect whether a Smart Contract (i.e. a contract between two parties concerning the exchange of goods and services, it is executed automatically and is governed by a source code written by a developer, contains clearly conditions accepted by both parties and resides on a distributed and decentralised network of computers) exhibits vulnerability. The proposed method relies on model checking and, through the adoption of μ-Calculus rules, is aimed to detect four different vulnerabilities on Smart Contracts modeled as automata. We preliminary evaluate the proposed method on a dataset composed by 40 (vulnerable and legitimate) contracts, by obtaining a precision ranging from 0.98 to 1 and a recall equal to 1, confirming that the proposed method can be promising in vulnerable Smart Contract detection.
Design of Campus Network Security System Based on Network Information Security

Network information security has been elevated to a national strategy and has become a powerful driving force for innovation and development. While the campus network is developing rapidly, due to the openness and sharing nature of the Internet technology, it still leaves shortcomings in the protection against illegal intrusion and attacks. With the subsequent increase in hacking techniques and the emergence of a wide range of network attack tools, the use of firewalls alone is no longer sufficient to deal with network attacks and to ensure effective protection of sensitive data. Research on the security system of campus network, propose a security system structure with longitudinal anti-attack capability and diversified defence means, adopt measures of application security, physical security, system security, network security and management security, etc. to enhance the ability of campus network to resist attacks and serve the teaching and research work of universities in all aspects.
A new secure and searchable data outsourcing leveraging a Bucket-Chain index tree

Data outsourcing aims to mitigate our concerns about data security and reliability by ensuring that the data is handled in a professional manner. Due to a lack of assurances regarding the reliability and security of data stored on servers, the increasing use of outsourcing is causing users concern. There have been some attempts to deal with these issues by implementing Secret Sharing-based Data Outsourcing (SSDO) schemes. The efficiency decline of SSDO schemes prompted researchers to use index trees. By releasing some secret information, index trees can be subject to frequency analysis. We propose a new type of index tree called Bucket-Chain B+Tree (BCB+Tree) that thwarts frequency analysis by keeping indexes in a chain of buckets. Therefore, as opposed to B+Tree, BCB+Tree’s exposure will not provide any information to outsiders, increasing the security of the scheme. Leveraging BCB+Tree, we have proposed an efficient and secure SSDO scheme. A number of implementations suggest that the proposed scheme performs better than the current schemes. Additionally, it has been shown that the adversary cannot recover the shared values by accessing k−1 data servers and the index tree.
Defining Factors That Support or Hinder Commercially Available Augmented Reality (AR) Devices for Construction Communication

Augmented reality (AR) presents virtual content in its physical context. This technology can enable construction professionals on-site and off-site to communicate using virtual annotations to highlight contextually-relevant details. Theoretically, AR has the potential to reduce the amount of site visits and the time spent physically traveling to and from projects. However, the majority of studies exploring AR commercially available devices present results from controlled implementations or laboratory environments. While those help for initial experimentation, they do not involve all the critical factors that can be present on active construction sites. Therefore, this work explores the technological performance of a commercially available AR device in an uncontrolled construction setting. The factors that enable or hinder AR performance are documented. The findings help construction practitioners strategically target the contexts where the technology thrives, while also helping researchers define more robust technology and environment workflows to enhance the functionality of current-generation AR devices.
Membership Feature Disentanglement Network

Membership inference (MI) determines whether a given data point is involved in the training of target machine learning model. Thus, the notion of MI relies on both the data feature and the model. The existing MI methods focus on the model only. We introduce a membership feature disentanglement network (MFDN) to approach MI from the perspective of data features. We assume that the data features can be disentangled into the membership features and class features. The membership features are those that enable MI, and class features refer to those that the network is trying to learn. MFDN disentangles these features by adversarial games between the encoders and auxiliary critic networks. It also visualizes the membership features using an inductive bias from the perspective of MI. We perform empirical evaluations to demonstrate that MFDN can disentangle membership features and class features.
Benchmarking Answer Set Programming systems for resource allocation in business processes

Declarative logic programming formalisms are well-suited to model various optimization and configuration problems. In particular, Answer Set Programming (ASP) systems have gained popularity, for example, to deal with scheduling problems present in several domains. The main goal of this paper is to devise a benchmark for ASP systems to assess their performance when dealing with complex and realistic resource allocation with objective optimization. To this end, we provide (i) a declarative and compact encoding of the resource allocation problem in ASP (compliant with the ASP Core-2 standard), (ii) a configurable ASP systems benchmark named BRANCH that is equipped with resource allocation instance generators that produce problem instances of different sizes with adjustable parameters (e.g., in terms of process complexity, organizational and temporal constraints), and (iii) an evaluation of four state-of-the-art ASP systems using BRANCH. This solid application-oriented benchmark serves the ASP community with a tool that leads to potential optimizations and improvements in encodings and further drives the development of ASP solvers. On the other hand, resource allocation is an important problem that still lacks adequate automated tool support in the context of Business Process Management (BPM). The ASP problem encoding, ready-to-use ASP systems and problem instance generators benefit the BPM community to tackle the problem at scale and mitigate the lack of openly available problem instance data.