Reviewerly/exHarmony huggingface dataset:
Constitutional Impediments In Criminal Procedure Identification Act 2022: At A Glance

An investigation is a critical component of criminal procedure. Investigation is closely related to collection of evidence and identification of the accused. The Criminal Procedure (Identification) Act, 2022 has significantly altered criminal identification methods. It supersedes the Identification of Prisoners Act of 1920. The concept of measurements was restricted in previous legislation, but it now encompasses almost every biological material as a measurement. The information specified under the Act forms part of the personal data of individuals and is thus protected under the right to privacy of individuals. In 2017 Supreme Court of India in historic judgment has held that the right to privacy is a fundamental right. It may also fail Article 14 requirements of a law to be fair and reasonable and for equality under the law. The important challenges involved in this Act are that data can be collected not only from convicted individuals, but also from individuals arrested for any offence and from any other person to aid an investigation. Compulsory taking of measurements of an accused would come within the mischief of Article 20 (3). Act enables coercive drawing of samples and possibly involves a violation of Article 20(3), which protects the right against self-incrimination. Therefore, scope for arbitrariness is present in this Act which can possibly misused by the police authorities.
Comparison of disclosures and legitimacy strategies employed after a cybersecurity incident: the case of Desjardins

The magnitude of the personal data breach at Desjardins financial cooperative in 2019 raises legitimacy concerns for Desjardins and its peers. Our study examines changes in the scope of cybersecurity disclosures and the legitimation strategies used in Desjardins's annual reports and compares them to those of other Canadian companies in the financial, communications and technology sectors. Based on a content analysis grid developed by Héroux and Fortin (2020), our results for Desjardins show an increase in disclosures related to the incident to repair damages to the organisation's legitimacy, but little change otherwise. Other companies in the sample, which need to maintain their legitimacy, also had very few changes in their cybersecurity disclosures. Our results reveal legitimation strategies that differ from past reactions to comparable legitimacy threatening incidents. Our contributions highlight conformity strategies targeting the incident for Desjardins and reluctance to change cybersecurity disclosures both for Desjardins and for its peers.
Design and Implementation of College English Online Learning Platform

The rapid development of the international process has made English the most widely used foreign language. However, due to the limited enrollment scope, uneven distribution of high-quality teachers' resources, high cost of classroom setting, single teaching form, and poor flexibility of students' independent class time, its further development is limited. The online learning platform for college English is designed and implemented. The platform comprehensively improves college English ability from four aspects of listening, reading, dialogue and writing, supports "one to many", "one to one" and other learning methods, and truly turns the classroom initiative to students. The overall architecture design of the platform has been completed, and the platform function modules have been designed. The platform functions are divided into five modules: user service, educational administration management, online learning, platform operation, and recommendation system. The system server uses the Spring MVC framework, structured business logic data is stored in the MySQL database, unstructured audio and video information can be stored in video files, and the front-end uses the Vue framework, The JSON data format is used for front and back data interaction. In the recommendation system module, collaborative filtering algorithm is used to achieve personalized recommendation of courses and popular essence posts.
An analysis of the research status of Information technology curriculum construction and teaching in high school

Under the background of the promulgating of the Curriculum Standard of Information Technology for General High School (2107 Edition), this paper adopts the literature analysis method, classifies and analyzes relevant papers, and tries to summarize the research content and focus of information technology curriculum in high school in terms of teaching (such as teaching mode). This paper has a reference and reference value for the research of information technology curriculum construction and teaching in high school.
An Experimental Study on Influence of Action-oriented Education Paradigm on Students' Key Capabilities

This article attempts to study and explore the impact of action-oriented education paradigm on students' key capabilities by using the questionnaire and experimental research. Through the study, the following conclusions are drawn: 1. Action-oriented education paradigm improves learners' abilities of self-planning, self-monitoring, self-selection, self-reflection and self-evaluation; 2. Action-oriented education paradigm improves learners' communicative competence, team spirit and sense of cooperation; 3. The action-oriented education paradigm increases learners' sense of self-efficacy and responsibility. However, the study also shows that learners still have problems in overcoming difficulties, concentrating and other personality qualities, especially persistence and willpower need to be further cultivated.
Toleration Factors: The Expectations of Decorum, Civility, and Certainty on Rural Social Media

This paper presents the understanding of the rural perception of toleration on Facebook by Bangladeshi users. In a three-year long ethnographic study with villagers in 12 rural areas in Jessore, Bangladesh, we investigated the three major aspects of toleration: decorum, civility, and certainty. Our findings informed that these factors influenced rural villagers’ interaction on social media. We found that to address these concerns, rural people often adopt their own framework where they co-existed with their own opinion along with the parties whose opinions were disagreeable to them. Here, the major concerns were unpredictable behavior of social media algorithms, conflicts of ideologies and cultural values, and mismatching language skills while using Facebook collectively and collaboratively within the family. We also found that the practice of toleration and associated factors influenced users’ interaction with other users both online and in the physical world. Building on our findings, we argue that understanding different conceptualizations of toleration is essential to have a safer digital space for marginalized rural communities. Our study contributes to HCI’s and ICTD’s understanding of disagreement and conflicts by bringing in the concept of toleration and helps design better appropriate Facebook experience for the users in rural Bangladesh and other similar communities.
Research on College English Classroom Teaching Design Based on Computer Multimedia Technology

Information technology, especially multimedia technology, with its strong interactive, visible and experiential nature, can significantly increase students' enthusiasm for active learning and the creation of awareness, and has more significant advantages for utilization. Computer multimedia technology is able to show the content that users want to convey in the form of audio and animation, which enriches the way of information dissemination and can show more space for development in future life. Nowadays, multimedia technology has been widely used in all aspects of real life and has achieved certain effects, especially in teaching activities through the use of multimedia technology, which can bring more convenience to English teaching. On this basis, this paper first analyzes the characteristics and utilization value of computer multimedia technology, then studies the design of English classroom teaching based on computer multimedia technology, and finally gives the application strategy of multimedia technology in the design of college English classroom. Due to the continuous development of computer information technology, multimedia has gradually become a new way to change teaching methods, and the use of computer multimedia technology during actual teaching can lead to effective improvement of English teaching effect, and promote the whole teaching content to have more vivid characteristics and enhance the teaching effect.
TINYCOD: Tiny and Effective Model for Camouflaged Object Detection

This paper introduces an effective and tiny model for real-time Camouflaged Object Detection (COD) named Tiny-COD. It achieves high performance with very low costs (Parameters < 5M, FLOPs < 1.5G), which can be applied on mobile devices. Specifically, we introduce a simple but effective Adjacent Scale Features Fusion module (ASFF), which can significantly enhance the representation ability of features from a lightweight backbone. Besides, as the edge areas of the camouflaged object often blend into the background, we carefully design an Edge Area Focus module (EAF) to solve this problem. Experimental results on COD datasets prove that the proposed method achieves state-of-the-art performance compared with other methods.
Multi-Speaker Multi-Lingual VQTTS System for LIMMITS 2023 Challenge

In this paper, we describe the systems developed by the SJTU X-LANCE team for LIMMITS 2023 Challenge, and we mainly focus on the winning system on naturalness for track 1. The aim of this challenge is to build a multi-speaker multi-lingual text-to-speech (TTS) system for Marathi, Hindi and Telugu. Each of the languages has a male and a female speaker in the given dataset. In track 1, only 5 hours data from each speaker can be selected to train the TTS model. Our system is based on the recently proposed VQTTS that utilizes VQ acoustic feature rather than mel-spectrogram. We introduce additional speaker embeddings and language embeddings to VQTTS for controlling the speaker and language information. In the cross-lingual evaluations where we need to synthesize speech in a cross-lingual speaker’s voice, we provide a native speaker’s embedding to the acoustic model and the target speaker’s embedding to the vocoder. In the subjective MOS listening test on naturalness, our system achieves 4.77 which ranks first.
Ancient Chinese Word Segmentation and Part-of-Speech Tagging Using Distant Supervision

Ancient Chinese word segmentation (WSG) and part-of-speech tagging (POS) are important to study ancient Chinese, but the amount of ancient Chinese WSG and POS tagging data is still rare. In this paper, we propose a novel augmentation method of ancient Chinese WSG and POS tagging data using distant supervision over parallel corpus. However, there are still mislabeled and unlabeled ancient Chinese words inevitably in distant supervision. To address this problem, we take advantage of the memorization effects of deep neural networks and a small amount of annotated data to get a model with much knowledge and a little noise, and then we use this model to relabel the ancient Chinese sentences in parallel corpus. Experiments show that the model trained over the relabeled data outperforms the model trained over the data generated from distant supervision and the annotated data. Our code is available at https://github.com/farlit/ACDS.
Int-GNN: A User Intention Aware Graph Neural Network for Session-Based Recommendation

Session-Based Recommendation (SBR) is a spotlight research problem. Although many efforts have been made, challenges still exist. The key to unlocking this shackle is the user intention, an intuitive but hard-to-model concept in the anonymous session. Unlike previous research, we suggest mining potential user intention by counting the number of item occurrences in a user session and considering the long interval between item re-interactions. Beyond these, we take user preference, a biased user intention, into account in the prediction stage. Forming these together, we propose a model named user Intention aware Graph Neural Network (Int-GNN) aiming at capturing user intention. Extensive experiments have been conducted on three real-world datasets, and the results show the superiority of our method. The code is available on GitHub: https://github.com/xuguangning1218/IntGNN_ICASSP2023
Anomalous Signal Detection for Cyber-Physical Systems Using Interpretable Causal Neural Network

Anomalous signal detection aims to detect unknown abnormal signals of machines from normal signals. However, building effective and interpretable anomaly detection models for safety-critical cyber-physical systems (CPS) is rather difficult due to the unidentified system noise and extremely intricate system dynamics of CPS and the neural network black box. This work proposes a novel time series anomalous signal detection model based on neural system identification and causal inference to track the dynamics of CPS in a dynamical state-space and avoid absorbing spurious correlation caused by confounding bias generated by system noise, which improves the stability, security and interpretability in detection of anomalous signals from CPS. Experiments on three real-world CPS datasets show that the proposed method achieved considerable improvements compared favorably to the state-of-the-art methods on anomalous signal detection in CPS. Moreover, the ablation study empirically demonstrates the efficiency of each component in our method.
Decaying Contrast for Fine-Grained Video Representation Learning

Prior contrast-based methods for video representation learning mainly focus on clip discrimination while ignoring the context and relationship of clips from the same video in the temporal dimension. As a consequence, the learned spatiotemporal representations for successive clips are inconsistent and hence perform poorly in fine-grained downstream tasks such as video fragment retrieval or localization. In this paper, we propose a decaying strategy to grasp the gradual evolution along the temporal dimension for fine-grained spatiotemporal representation learning, which consists of two novel contrastive losses. The external decaying contrastive loss is designed to increase the relative similarity of clips from the same video while the internal decaying contrastive loss aims to maintain the discrimination of clips. Experimental results show that the proposed decaying contrastive training approach achieves a significant improvement in the fine-grained video retrieval task on multiple benchmark datasets.
Spatiotemporal Anomaly Object Detection Using Edge Cloud Collaborative For Real-Time Surveillance Video

Video Object Detection is a method that enables computer systems to examine video and identify items in the scene. Object detection is computer vision technology that tries to find items in images or videos. The objective of Video Object Detection is to enable the system to detect, identify, and categorize items in the frame. In normal closed-circuit television footage, only the videos are captured and recorded. So, in order to trigger the detection of events automatically, an Internet of Things-enabled camera system was used. In the Internet of Things applications, the videos 0are stored and processed in a centralized server which causes time latency. In order to overcome this problem, a simple hybrid instantaneous online video object detection system was proposed. In this system, an edge and cloud collaborative technology was used. The current video object detection methodology uses Deep Neural Network to detect objects present in the scene. For real-time streaming video, the object could not be detected accurately using Deep Convolutional Neural Network because of low end-to-end latency, and also it was not applicable for latency-sensitive Internet of Things applications. The proposed work focuses on detecting vehicles traveling in the wrong lane of the road by analyzing real-time surveillance video. For detecting this anomaly, an edge cloud computing technology was proposed. In this technology, data are analyzed using the Kalman filter algorithm and with an entry-exit algorithm, and the result was stored in the cloud. By implementing the above technologies, both processing and communication time are reduced and instantaneous anomaly detection was achieved.
Deep Learning Models for Video-based Hand Gesture Recognition in Robotic Applications

Vision-based hand gestures recognition is often employed to remotely control machines and robots. In this work, we perform a comparison among multiple convolutional neural networks (CNNs) models, namely VGG16, InceptionNet, EfficientNet, and a self-designed CNN model for recognition of multiple hand gestures captured using a video camera. The video dataset has been self-collected for 24 dynamic hand gestures performed by 5 users. Key-frames are extracted from the input videos and processed in the considered CNN models for classification. The models are optimized for classification by varying the learning rates and the type of optimizer used during training. While VGG16, InceptionNet and the proposed CNN model attain the highest classification accuracy of 99.3055; the proposed CNN model consists of the least number of trainable paramters. Hence, the proposed CNN model is a light-weight model, which is suitable for deployment on edge device while it yields performance comparable with the well-known CNN models.
A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification

Electrocardiogram (ECG) signals are recordings of the heart's electrical activity and are widely used in the medical field to diagnose various cardiac conditions and monitor heart function. The accurate classification of ECG signals is crucial for the early detection and treatment of heart-related diseases. This paper proposes a novel approach based on an improved differential evolution (DE) algorithm for ECG signal classification. To this end, after the preprocessing step, we extracted several features such as BPM, IBI, and SDNN. Then, the features are fed into a multi-layer perceptron (MLP). While MLPs are still widely used for ECG signal classification, using gradient-based training methods, the most widely used algorithm for the training process, has significant disadvantages, such as the possibility of being stuck in local optimums. Population-based metaheuristic techniques have been effectively used to address this. This paper employs an enhanced differential evolution (DE) algorithm for the training process as one of the most effective population-based algorithms. To this end, we improved DE based on a clustering-based strategy, opposition-based learning, and a local search. Clustering-based strategies can act as crossover operators, while the goal of the opposition operator is to improve the exploration of the DE algorithm. The weights and biases found by the improved DE algorithm are then fed into six gradient-based local search algorithms. In other words, the weights found by the DE are employed as an initialization point. Therefore, we introduced six different algorithms for the training process (in terms of different local search algorithms). In an extensive set of experiments, we showed that our proposed training algorithm could provide better results than the conventional training algorithms.
ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics

Many machine learning (ML) libraries are accessible online for ML practitioners. Typical ML pipelines are complex and consist of a series of steps, each of them invoking several ML libraries. In this demo paper, we present ExeKGLib, a Python library that allows users with coding skills and minimal ML knowledge to build ML pipelines. ExeKGLib relies on knowledge graphs to improve the transparency and reusability of the built ML workflows, and to ensure that they are executable. We demonstrate the usage of ExeKGLib and compare it with conventional ML code to show its benefits.
Quantifying medical histories with the Kaplan-Meier (KM) estimator for feature extraction of electronic health records in machine learning

Abstract This protocol aimed to describe data transformation procedure of medical histories from electronic health records (EHRs) to historical rates by Kaplan-Meier (KM) estimation. The applicability is to extract features from real-world, time-varying data of EHRs, for developing but not limited to a machine learning prediction model. By this extraction technique, machine can learn medical history of a condition in each healthcare provider, as a differential quantity through time in term of affecting a future health state, without a need to access EHRs of other healthcare providers. However, this protocol needs a sufficient amount of longitudinal data from the most subjects in EHRs. The key stages consisted of time interval computation, historical rate derivation, and data transformation into historical rates.
Learning under concept drift and non-stationary noise: Introduction of the concept of persistence

Learning from noisy data is a challenging task especially when the system under consideration has a non-stationary nature. The source of the noise is often assumed to be stationary, however the severity or characteristics of noise may also be time-varying, which causes multiple sources of drift in the collected data. This study introduces a novel adaptive learning rate approach to improve learning when the observations from a non-stationary system is altered by an also non-stationary source of noise. As an example to this approach, we propose Persistence Aware Robust Learner (PeARL), an online learning method that utilizes a novel concept called persistence, which is a local noisiness estimation metric to measure the correspondence of a signal to discrete white noise. Making use of this metric, PeARL is able to adaptively adjust the learning rate for each observation during learning to reduce the effect of noise. With this level of control on the learning rate, noisy instances have less disruptive effect on the maintained estimate. We experimentally evaluate PeARL on (a) systematically generated synthetic data and (b) real-world data, including accelerometer readings collected from people (HASC2010corpus) and current measurements from electric motors collected within the scope of EU-funded research project iRel40. The experiments reveal a favorable region of noise rate, in which the proposed method achieves up to 40% reduction in mean absolute error (MAE).
Exploration and practice of the run-through cultivation mode for high-end technical and skilled talents---Taking Beijing university of information technology as an example

Run-through Cultivation is an innovative training mode supported by Beijing Municipal Education Commission. It is jointly trained by high-level vocational colleges with Beijing characteristics and application-oriented undergraduates, to connect with advantageous majors in industrial development, and develop talents with high-end technical skills. On the basis of applied higher education, our university increases the training of technical skills at the undergraduate level. Relying on the professional advantages of undergraduate colleges to open up the growth channel of technical skills talents, and cooperate with a number of higher vocational colleges to explore through training mode. Clarify the orientation, training objectives and the training mode of through-education, drive the optimal allocation of educational resources, make full use of the school's high-quality characteristic resources, avoid the homogenization with ordinary undergraduate training methods, and highlight the through-training characteristics of high-end technical and skilled talents. Integration of production and education, cooperation between schools and enterprises to educate people, adhere to the combination of moral and technical training, education and training, to cultivate high-quality technical and skilled personnel. Practice has proved that the effect is good.