Reviewerly/exHarmony huggingface dataset:
Granular classifier: Building traffic granules for encrypted traffic classification based on granular computing

Accurate classification of encrypted traffic plays an important role in network management. However, current methods confronts several problems: inability to characterize traffic that exhibits great dispersion, inability to classify traffic with multi-level features, and degradation due to limited training traffic size. To address these problems, this paper proposes a traffic granularity-based cryptographic traffic classification method, called Granular Classifier (GC). In this paper, a novel Cardinality-based Constrained Fuzzy C-Means (CCFCM) clustering algorithm is proposed to address the problem caused by limited training traffic, considering the ratio of cardinality that must be linked between flows to achieve good traffic partitioning. Then, an original representation format of traffic is presented based on granular computing, named Traffic Granules (TG), to accurately describe traffic structure by catching the dispersion of different traffic features. Each granule is a compact set of similar data with a refined boundary by excluding outliers. Based on TG, GC is constructed to perform traffic classification based on multi-level features. The performance of the GC is evaluated based on real-world encrypted network traffic data. Experimental results show that the GC achieves outstanding performance for encrypted traffic classification with limited size of training traffic and keeps accurate classification in dynamic network conditions.
VERI: A Large-scale Open-Source Components Vulnerability Detection in IoT Firmware

IoT device manufacturers integrate open-source components (OSCs) to serve necessary and common functions for facilitating firmware development. However, outdated versions of OSC conceal N-day vulnerabilities and continue to function on IoT devices. The security risks can be predicted once we can identify the OSC versions employed in the firmware. Existing works make attempts at OSC version identification but fail to perform vulnerability detection on a large-scale IoT firmware due to i) unsuitable version identification method for IoT firmware scenario. ii) the lack of a large-scale version-vulnerability relation database. To this end, we propose a system VERI for large-scale vulnerability detection based on lightweight version identification. First, for OSC version identification, VERI leverages symbolic execution with static analysis to identify exact OSC versions even though there are many version-like strings in OSC. Second, VERI employs a deep learning-based method to extract OSC names and vulnerable version ranges from vulnerability descriptions, constructs and maintains an OSC version-vulnerability relation database to serve the vulnerability detection. Finally, VERI polls the relation database to confirm the N-day security risk of the OSC with identified version. The evaluation results show that VERI achieves 96.43% accuracy with high efficiency in OSC version identification. Meanwhile, the deep learning model accurately extracts the OSC names and versions from vulnerability descriptions dataset with 97.19% precision and 96.56% recall. Based on the model, we build a large-scale version-vulnerability relation database. Furthermore, we utilize VERI to conduct a large-scale analysis on 28,890 firmware and find 38,654 vulnerable OSCs with 266,109 N-day vulnerabilities, most of which are with high risks. From the detection results, we find that after the official patch for the vulnerability is released, manufacturers delay an average of 473 days to patch the firmware.
NIM-Nets: Noise-Aware Incomplete Multi-View Learning Networks

Data in real world are usually characterized in multiple views, including different types of features or different modalities. Multi-view learning has been popular in the past decades and achieved significant improvements. In this paper, we investigate three challenging problems in the field of incomplete multi-view representation learning, namely, i) how to reduce the influences produced by missing views in multi-view dataset, ii) how to learn a consistent and informative representation among different views and iii) how to alleviate the impacts of the inherent noise in multi-view data caused by high-dimensional features or varied quality for different data points. To address these challenges, we integrate these three tasks into a problem and propose a novel framework termed Noise-aware Incomplete Multi-view Learning Networks (NIM-Nets). NIM-Nets fully utilize incomplete data from different views to produce a multi-view shared representation which is consistent, informative and robust to noise. We model the inherent noise in data by defining the distribution Γ and assuming that each observation in the incomplete dataset is sampled from the distribution Γ. To the best of our knowledge, this is the first work to unify learning the consistent and informative representation, alleviating the impacts of noise in data and handling the view-missing patterns in multi-view learning into a framework. We also first give a definition of robustness and completeness for incomplete multi-view representation learning. Based on NIM-Nets, we present joint optimization models for classification and clustering, respectively. Extensive experiments on different datasets demonstrate the effectiveness of our method over the existing work based on classification and clustering tasks in terms of different metrics.
Integrating culture in future speculation: Speculating Chinese wedding futures

Futures and design are intensively collaborating with each other in the field of experiential futures, with critical design as a source of inspiration. Rather than focusing on the imagination and speculation of future technology as most critical design genres do, this paper examines critical design from a cultural perspective and explores approaches through which future as a way of life is speculated. Special attention has been paid on Design for the Speculative Future (DSF), a culture-centred critical design approach that describes future scenarios in design languages. A framework for integrating cultural resources in the future speculation process has been proposed, taking Chinese weddings as an exemplary case study. Two DSF studios were conducted online in a Chinese social-cultural context to formulate and examine this framework. The proposed framework will benefit both design beginners at a practical level to adopt it in the process of speculating futures, and researchers at the intersection of design and futures to examine how critical design links the present and future and has an impact on futures through construction of future scenarios.
Generalization Ability Improvement of Speaker Representation and Anti-Interference for Speaker Verification

The ability to generalize to mismatches between training and testing conditions and resist interference from other speakers is crucial for the performance of speaker verification. In this paper, we propose two novel approaches to improve the generalization ability to deal with the mismatched recorded scenarios and languages in test conditions and to reduce the influence of interference from other speakers on the similarity measurement of two speaker embeddings. First, parent embedding learning (PEL) is used for model training, which exploits the generalization ability of the shared structure to improve the representation of speaker embeddings. Second, partial adaptive score normalization (PAS-Norm) is used to reduce the influence of interference from other speakers on embedding-based similarity measures. In the experiments, the speaker embedding models are trained using the VoxCeleb2 dataset, and the performance is evaluated on four other datasets under different conditions, including VoxCeleb1, Librispeech, SITW, and CN-Celeb datasets. In the experiments on VoxCeleb1, evaluation results considering a large number of verification speakers and identity restrictions show that the proposed PEL-based system reduces the EER by 6.0% and 4.9% in these two cases, respectively, compared to the state-of-the-art (SOTA) system. Furthermore, in the experiments evaluating speaker verification in mismatch conditions on SITW and CN-Celeb, the proposed PEL-based system also outperforms the SOTA system. In the language mismatched conditions, the EER is reduced by 8.3%. For the evaluation of the influence of interference from other speakers, the EER is significantly reduced by 24.4% when PAS-Norm is used instead of the baseline AS-Norm score normalization method.
A Qualitative Study on Rethinking the Values of University with Changes in the Environment of Higher Education

As educational institutions with a long history, universities have constantly faced new challenges. Recently, universities are once again facing a moment of change. They are experiencing unprecedented environmental changes, such as demographic changes, socio-economic changes brought on by the 4th Industrial Revolution, changes in the number of high school graduates, and changes due to the Corona virus pandemic. Amid such momentous changes in the environment, social demands for qualitative development in university education are increasing. With this in mind, the purpose of this study is to rethink the values of university education. In this study, the qualitative research method was used to analyze how the key stakeholders of university education view changes in certain values of university education amid rapid environmental transformations. The key stakeholders were grouped into four groups: professors, students and youth, corporate officials, and policy-related personnel, and semi-structured interviews were conducted for each group. The results of the interview were compared for each group: the current status and problems of university education and the changes in the values of university education according to environmental changes (‘constant values’, ’values to become less important’, and ‘values to become more important’). As a result of the study, the consensus of each group was the need for changes in university education tailored to the changes of students called as the Z generation, the need to change the operating method in order to take advantage of the university’s strengths, and a decrease in the value of credits and degrees themselves. Different perspectives were given according to the aforementioned groups regarding the influence of academic background or desirable policy directions for changes in university education. Based on these research results, we discussed what should be taught in universities and how to secure accountability and publicity without undermining the autonomy of the university.
A novel Mahalanobis distance method for predicting oil and gas resource spatial distribution

Accurate prediction of spatial distribution of petroleum resources is important for petroleum exploration. Mahalanobis distance is a popular and effective method to predict the spatial distribution of oil and gas. However, this method has equal weights for each variable, exaggerates secondary variables, and is prone to misjudge when the distance is close or equal, which impairs the accuracy of classification. To solve these problems, this paper proposes a novel Mahalanobis distance method based on genetic algorithm (GA-MD) to optimize attribute weights. The Sangonghe Formation in the hinterland of the Junggar Basin was used as an example, the validity of GA-MD was evaluated in the exploratory well data set. Compared with the current mainstream methods, the results show that the accuracy of GA-MD method is the highest, and the accuracy is improved by 2–6.2%. The application effect of the proposed method is verified by the prediction result of oil and gas probability map, the GA-MD method not only shows higher oil and gas bearing probability in the reserve areas but also has better trend extrapolation ability compared with other methods. Based on the GA-MD results, the favorable zones with remaining petroleum resources in the Sangonghe Formation in the hinterland of the Junggar Basin were visualized. Three types of favorable oil and gas distribution areas are selected. The favorable areas provide a basis for quantitative decision-making for the optimization of the next drilling strategy and determination of oil and gas exploration deployment direction in the study area.
A Novel User-Friendly Application for Foreground Detection with Post-Processing in Surveillance Video Analytics

Detection of the object in the video is a primary task of all video-processing-based applications. It is one of the challenging areas in computer vision. The paper presents a novel MATLAB-based object detection application based on an improved Gaussian Mixture Model. Gaussian Mixture Model with post-processing applied here for segmentation of foreground from background. The application is divided into three modules pre-processing, detection and post-processing. The morphological gradient filter uses here for segmenting the foreground objects from the background. The proposed method was tested for various video sequences with challenges and proved that the approach performs well for the majority video sequences.
Comparative analysis of TF-IDF and loglikelihood method for keywords extraction of twitter data

Twitter has become the foremost standard of social media in today’s world. Over 335 million users are online monthly, and near about 80% are accessing it through their mobiles. Further, Twitter is now supporting 35+ which enhance its usage too much. It facilitates people having different languages. Near about 21% of the total users are from US and 79% of total users are outside of US. A tweet is restricted to a hundred and forty characters; hence it contains such information which is more concise and much valuable. Due to its usage, it is estimated that five hundred million tweets are sent per day by different categories of people including teacher, students, celebrities, officers, musician, etc. So, there is a huge amount of data that is increasing on a daily basis that need to be categorized. The important key feature is to find the keywords in the huge data that is helpful for identifying a twitter for classification. For this purpose, Term Frequency-Inverse Document Frequency (TF-IDF) and Loglikelihood methods are chosen for keywords extracted from the music field and perform a comparative analysis on both results. In the end, relevance is performed from 5 users so that finally we can take a decision to make assumption on the basis of experiments that which method is best. This analysis is much valuable because it gives a more accurate estimation which method’s results are more reliable.
Auxiliary Loss for BERT-Based Paragraph Segmentation

Paragraph segmentation is a text segmentation task. Iikura et al. achieved excellent results on paragraph segmentation by introducing focal loss to Bidirectional Encoder Representations from Transformers. In this study, we investigated paragraph segmentation on Daily News and Novel datasets. Based on the approach proposed by Iikura et al., we used auxiliary loss to train the model to improve paragraph segmentation performance. Consequently, the average F1-score obtained by the approach of Iikura et al. was 0.6704 on the Daily News dataset, whereas that of our approach was 0.6801. Our approach thus improved the performance by approximately 1%. The performance improvement was also confirmed on the Novel dataset. Furthermore, the results of two-tailed paired t-tests indicated that there was a statistical significance between the performance of the two approaches.
Testing ground-truth errors in an automotive dataset for a DNN-based object detector

Given the promising advances in the field of Assisted and Automated Driving, it is expected that the roads of the future will be populated by vehicles driven by computers, partially or fully replacing human drivers.In this scenario, the first stage of the perception-decision-actuation pipeline will likely rely on Deep Neural Networks for understanding the scene around the vehicle.Typical tasks for Deep Neural Networks are object detection and instance segmentation, tasks relying on supervised learning and annotated datasets.As one can imagine, the quality of the labelled dataset strongly affects the performance of the network, and this aspect is investigated in this paper.Annotation quality should be a primary concern in safety-critical tasks, such as Assisted and Automated Driving.This work addresses and classifies some of the mistakes found in a popular automotive dataset.Moreover, some experiments with a Deep Neural Network model were performed to test the effect of these mistakes on network predictions.A set of criteria was established to support the relabelling of the testing dataset which was compared to the original dataset.
3D human pose estimation in multi-view operating room videos using differentiable camera projections

3D human pose estimation in multi-view operating room (OR) videos is a relevant asset for person tracking and action recognition.However, the surgical environment makes it challenging to find poses due to sterile clothing, frequent occlusions and limited public data.Methods specifically designed for the OR are generally based on the fusion of detected poses in multiple camera views.Typically, a 2D pose estimator such as a convolutional neural network (CNN) detects joint locations.Then, the detected joint locations are projected to 3D and fused over all camera views.However, accurate detection in 2D does not guarantee accurate localisation in 3D space.In this work, we propose to directly optimise for localisation in 3D by training 2D CNNs end-to-end based on a 3D loss that is backpropagated through each camera's projection parameters.Using videos from the MVOR dataset, we show that this end-to-end approach outperforms optimisation in 2D space.
Recognition of American Sign Language Using Deep Convolution Network

Around 466 million individuals are deaf or hard of hearing worldwide. Those individuals must engage with others, learn new things, and participate in activities. Sign language serves as a link between them and the rest of the world. There are numerous problems in creating a system that can automatically recognize sign language. For that, we have developed models for American Sign Language, we used a dataset that represents the American Sign Language Alphabet, which includes 29 classes for all English sign language letters as well as three additional characters: "del", "nothing" and "space." The collection contains 10,208 in image format, with each class presented in a separate folder. 80 percent of the dataset was used for training, while 20 percent was used for testing. The accuracy of the results for the proposed system was 100%.
Privacy and Security Issues in Blockchain based IoT Systems: Challenges and Opportunities

The Internet of Things and blockchain technology will be the focus of this research. This study looks at issues of security and privacy. The main challenges it faces, as well as the possibility of using it to improve bitcoin transactions for businesses, are the focus points. The blockchain concept is a distributed ledger with decentralization and data that is dispersed evenly across all nodes in the chain. In terms of data security, the blockchain ensures all three components of the CIA trinity by maintaining data confidentiality, integrity, and availability. Initially, the significance of this technique was limited to crypto currency because it is a one-way function that prevents any type of record fabrication. This concept is currently widely used in medical and public health data security, and many financial institutions, such as Citi Bank, are exploring employing block chain technology to create a breakthrough in the financial realm. This research is a comprehensive review of the literature on the significance of blockchain technology, as well as the security and privacy issues that this technology now faces.
Remarks on hyperspaces for Priestley spaces

The Vietoris space of a Stone space plays an important role in the coalgebraic approach to modal logic. When generalizing this to positive modal logic, there is a variety of relevant hyperspace constructions based on various topologies on a Priestley space and mechanisms to topologize the hyperspace of closed sets. A number of authors considered hyperspaces of Priestley spaces and their application to the coalgebraic approach to positive modal logic. A mixture of techniques from category theory, pointfree topology, and Priestley duality have been employed. Our aim is to provide a unifying approach to this area of research relying only on a basic familiarity with Priestley duality and related free constructions of distributive lattices.
Strengthened multiple correlation for multi-label few-shot intent detection

A challenging problem that arises in few-shot intent detection is the complexity of multiple intention (multi-label) detection. The prototypical network uses the mean value of support instances as label prototype, which cannot eliminate the interference among features of multiple labels, making the learned label prototypes deviate from the real label features. Meanwhile, regardless of the correlation with the label prototype, all the feature dimensions in the query instance are treated equally, which reduces the accuracy of similarity measurement between the query instance and the label prototype. In this paper, we propose a hybrid calculation of correlation for few-shot multi-label intent detection method (HCC-FSML) to overcome the problem. This method proposes an instance-level attention mechanism to focus on the instance representation with high correlation between support instances and positive labels, so as to improve the consistency between the label prototype and the real label features; In the similarity measurement, the feature-level attention mechanism is introduced to focus on the feature dimensions of query instance with high correlation with positive label prototype, so as to improve the accuracy of similarity measurement. The experiment shows that the proposed method achieves new state-of-the-art results in intent detection by focusing on typical features of instances and reducing multiple interference among labels.
Parameterizing density operators with arbitrary symmetries to gain advantage in quantum state estimation

In this work, we show how to parameterize a density matrix that has an arbitrary symmetry, knowing the generators of the Lie algebra (if the symmetry group is a connected Lie group) or the generators of its underlying group (in case it is finite). This allows to pose MaxEnt and MaxLik estimation techniques as convex optimization problems with a substantial reduction in the number of parameters of the function involved. This implies that, apart from a computational advantage due to the fact that the optimization is performed in a reduced space, the amount of experimental data needed for a good estimation of the density matrix can be reduced as well. In addition, we run numerical experiments and apply these parameterizations to quantum state estimation of states with different symmetries.