Reviewerly/exHarmony huggingface dataset:
Small moving vehicle detection via local enhancement fusion for satellite video

Satellite video is an emerging data source for dynamic Earth observation, which provides us with a new means for large-scale moving vehicle detection and traffic monitoring. However, the ratio of the foreground to background in satellite video is severely uneven. Furthermore, due to the high altitude and spatial resolution of video satellites, the moving vehicles often have a small size and low contrast with the background. There is also complex environment and background movement due to the imaging mode of satellite video, which makes the vehicle detection more challenging. In this paper, to solve the problems of the low contrast and the dynamic background in satellite video, a local enhancement fusion Gaussian mixture model and improved three-frame difference detection framework, namely E-GMMTFD, is proposed to detect small moving vehicles in satellite video. In the E-GMMTFD framework, the local contrast enhancement method is used to improve the contrast between the target and the background. The Gaussian mixture model (GMM) and the three-frame difference (TFD) method are fused by a logical operation to detect moving vehicles. The E-GMMTFD framework utilizes the adaptability to complex environment changes of the GMM and the strength of the TFD method to eliminate background movement, thus reducing the false alarms caused by illumination change and background movement. Three experiments were performed on moving vehicle detection using Jilin-1 satellite video and SkySat-1 satellite video. The experimental results confirmed that the proposed framework can be efficiently used to detect moving vehicles in the large scale of satellite video. Based on the vehicle detection results for the satellite video, the traffic density of the urban road network can provide technical support for alleviating urban traffic congestion.
Everything You Ever Wanted to Know About Bitcoin Mixers (But Were Afraid to Ask)

The lack of fungibility in Bitcoin has forced its userbase to seek out tools that can heighten their anonymity. Third-party Bitcoin mixers use obfuscation techniques to protect participants from blockchain transaction analysis. In recent years, various centralized and decentralized Bitcoin mixing methods were proposed in academic literature (e.g., CoinJoin, CoinShuffle). Although these methods strive to create a threat-free environment for users to preserve their anonymity, public Bitcoin mixers continue to be associated with theft and poor implementation. This paper explores the public Bitcoin mixer ecosystem to identify if today’s mixing services have adopted academia’s proposed solutions. We perform real-world interactions with publicly available mixers to analyze both implementation and resistance to common threats in the mixing landscape. We present data from 21 publicly available mixing services on the deep web and clearnet. Our results highlight a clear gap between public and proposed Bitcoin mixers in both implementation and security. We find that the majority of key security features proposed by academia are not deployed in any public Bitcoin mixers that are trusted most by Bitcoin users. Today’s mixing services focus on presenting users with a false sense of control to gain their trust rather than employing secure mixing techniques.
Use of Virtual Reality Technology in Animation Course Teaching

With the rapid development of Internet technology, in consequence of the outbreak of COVID-19, colleges and universities have postponed the start of term, in order to protect the life safety of teachers and students. However, to avoid affecting students’ academic studies, colleges and universities have taken various virtual reality technologies to implement personalized online teaching on students. However, there are still some problems in actual teaching, such as the monotonous teaching mode of animation course, which results in students’ lack of practical animation operation ability; the lectures lay too much emphasis on the textbook content and the pedagogical knowledge in animation is not updated, etc. In our study, we will combine Animation Teaching mode with the educational concept of STEAM, and designs a teaching mode oriented to students’ needs. This teaching mode include five parts: analysis of students’ needs, analysis of learning goals, selection of learning strategies, design of learning activities and design of learning evaluation. At the same time, based on the American Customer Satisfaction Index (ACSI) Model, our study will also investigate student satisfaction from four dimensions: student expectations, students’ perceived quality, students’ perceived value and student loyalty, with a view to measure the effectiveness of this teaching model. Through the teaching practice, it is found that this teaching mode can significantly promote students’ grades and motivate their enthusiasm for learning. The students’ autonomic learning and mastery of professional knowledge are significantly superior to those of traditional multimedia teaching method. The proposed teaching mode offers an objective reference for the application of virtual reality technology in teaching.
iiit-indic-hw-words: A Dataset for Indic Handwritten Text Recognition

Handwritten text recognition (htr) for Indian languages is not yet a well-studied problem. This is primarily due to the unavailability of large annotated datasets in the associated scripts. Existing datasets are small in size. They also use small lexicons. Such datasets are not sufficient to build robust solutions to htr using modern machine learning techniques. In this work, we introduce a large-scale handwritten dataset for Indic scripts containing 868K handwritten instances written by 135 writers in 8 widely-used scripts. A comprehensive dataset of ten Indic scripts are derived by combining the newly introduced dataset with the earlier datasets developed for Devanagari (iiit-hw-dev) and Telugu (iiit-hw-telugu), referred to as the iiit-indic-hw-words.
Spiral Motion Enhanced Elite Whale Optimizer for Global Tasks

The whale optimization algorithm (WOA) is a high-performance metaheuristic algorithm that can effectively solve many practical problems and broad application prospects. However, the original algorithm has a significant improvement in space in solving speed and precision. It is easy to fall into local optimization when facing complex or high-dimensional problems. To solve these shortcomings, an elite strategy and spiral motion from moth flame optimization are utilized to enhance the original algorithm’s efficiency, called MEWOA. Using these two methods to build a more superior population, MEWOA further balances the exploration and exploitation phases and makes it easier for the algorithm to get rid of the local optimum. To show the proposed method’s performance, MEWOA is contrasted to other superior algorithms on a series of comprehensive benchmark functions and applied to practical engineering problems. The experimental data reveal that the MEWOA is better than the contrast algorithms in convergence speed and solution quality. Hence, it can be concluded that MEWOA has great potential in global optimization.
Data and Parameter Scaling Laws for Neural Machine Translation

We observe that the development crossentropy loss of supervised neural machine translation models scales like a power law with the amount of training data and the number of non-embedding parameters in the model.We discuss some practical implications of these results, such as predicting BLEU achieved by large scale models and predicting the ROI of labeling data in low-resource language pairs.
Analyzing Speaker Information in Self-Supervised Models to Improve Zero-Resource Speech Processing

Contrastive predictive coding (CPC) aims to learn representations of speech by distinguishing future observations from a set of negative examples. Previous work has shown that linear classifiers trained on CPC features can accurately predict speaker and phone labels. However, it is unclear how the features actually capture speaker and phonetic information, and whether it is possible to normalize out the irrelevant details (depending on the downstream task). In this paper, we first show that the per-utterance mean of CPC features captures speaker information to a large extent. Concretely, we find that comparing means performs well on a speaker verification task. Next, probing experiments show that standardizing the features effectively removes speaker information. Based on this observation, we propose a speaker normalization step to improve acoustic unit discovery using K-means clustering of CPC features. Finally, we show that a language model trained on the resulting units achieves some of the best results in the ZeroSpeech2021~Challenge.
Effectiveness of Pre-training for Few-shot Intent Classification

This paper investigates the effectiveness of pre-training for few-shot intent classification.While existing paradigms commonly further pre-train language models such as BERT on a vast amount of unlabeled corpus, we find it highly effective and efficient to simply finetune BERT with a small set of labeled utterances from public datasets.Specifically, fine-tuning BERT with roughly 1,000 labeled data yields a pre-trained model -IntentBERT, which can easily surpass the performance of existing pre-trained models for few-shot intent classification on novel domains with very different semantics.The high effectiveness of IntentBERT confirms the feasibility and practicality of few-shot intent detection, and its high generalization ability across different domains suggests that intent classification tasks may share a similar underlying structure, which can be efficiently learned from a small set of labeled data.The source code can be found at https://github.com/hdzhang-code/IntentBERT.
A Fine-Grained IoT Data Access Control Scheme Combining Attribute-Based Encryption and Blockchain

IoT technology has been widely valued and applied, and the resulting massive IoT data brings many challenges to the traditional centralized data management, such as performance, privacy, and security challenges. This paper proposes an IoT data access control scheme that combines attribute-based encryption (ABE) and blockchain technology. Symmetric encryption and ABE algorithms are utilized to realize fine-grained access control and ensure the security and openness of IoT data. Moreover, blockchain technology is combined with distributed storage to solve the storage bottleneck of blockchain systems. Only the hash values of the data, the hash values of the ciphertext location, the access control policy, and other important information are stored on the blockchain. In this scheme, smart contract is used to implement access control. The results of experiments demonstrate that the proposed scheme can effectively protect the security and privacy of IoT data and realize the secure sharing of data.
Sparse Feature Factorization for Recommender Systems with Knowledge Graphs

Deep Learning and factorization-based collaborative filtering recommendation models have undoubtedly dominated the scene of recommender systems in recent years. However, despite their outstanding performance, these methods require a training time proportional to the size of the embeddings and it further increases when also side information is considered for the computation of the recommendation list. In fact, in these cases we have that with a large number of high-quality features, the resulting models are more complex and difficult to train. This paper addresses this problem by presenting KGFlex: a sparse factorization approach that grants an even greater degree of expressiveness. To achieve this result, KGFlex analyzes the historical data to understand the dimensions the user decisions depend on (e.g., movie direction, musical genre, nationality of book writer). KGFlex represents each item feature as an embedding and it models user-item interactions as a factorized entropy-driven combination of the item attributes relevant to the user. KGFlex facilitates the training process by letting users update only those relevant features on which they base their decisions. In other words, the user-item prediction is mediated by the user's personal view that considers only relevant features. An extensive experimental evaluation shows the approach's effectiveness, considering the recommendation results' accuracy, diversity, and induced bias. The public implementation of KGFlex is available at https://split.to/kgflex.
Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems

Recommender Systems (RSs) in real-world applications often deal with billions of user interactions daily. To capture the most recent trends effectively, it is common to update the model incrementally using only the newly arrived data. However, this may impede the model’s ability to retain long-term information due to the potential overfitting and forgetting issues. To address this problem, we propose a novel Adaptive Sequential Model Generation (ASMG) framework, which generates a better serving model from a sequence of historical models via a meta generator. For the design of the meta generator, we propose to employ Gated Recurrent Units (GRUs) to leverage its ability to capture the long-term dependencies. We further introduce some novel strategies to apply together with the GRU meta generator, which not only improve its computational efficiency but also enable more accurate sequential modeling. By instantiating the model-agnostic framework on a general deep learning-based RS model, we demonstrate that our method achieves state-of-the-art performance on three public datasets and one industrial dataset.
Entanglement-Assisted Quantum Codes From Algebraic Geometry Codes

Quantum error-correcting codes play the role of suppressing noise and decoherence in quantum systems by introducing redundancy. Some strategies can be used to improve the parameters of these codes. For example, entanglement can provide a way for quantum error-correcting codes to achieve higher rates than the one obtained by means of the traditional stabilizer formalism. Such codes are called entanglement-assisted quantum error-correcting (EAQEC) codes. In this paper, we utilize algebraic geometry codes to construct several families of EAQEC codes derived from the Euclidean and the Hermitian construction. Three families constructed here consist of codes whose quantum Singleton defect is equal to zero, one, or two. We also construct families of EAQEC codes with an encoding rate exceeding the quantum Gilbert-Varshamov bound. Additionally, asymptotically good towers of linear complementary dual codes are used to obtain asymptotically good families of EAQEC codes consuming maximal entanglement. Furthermore, a simple comparison with the quantum Gilbert-Varshamov bound demonstrates that, by utilizing the proposed construction, it is possible to generate an asymptotically family of EAQEC codes that exceeds this bound.
STraTA: Self-Training with Task Augmentation for Better Few-shot Learning

Despite their recent successes in tackling many NLP tasks, large-scale pre-trained language models do not perform as well in few-shot settings where only a handful of training examples are available.To address this shortcoming, we propose STraTA, which stands for Self-Training with Task Augmentation, an approach that builds on two key ideas for effective leverage of unlabeled data.First, STraTA uses task augmentation, a novel technique that synthesizes a large amount of data for auxiliary-task fine-tuning from target-task unlabeled texts.Second, STraTA performs selftraining by further fine-tuning the strong base model created by task augmentation on a broad distribution of pseudo-labeled data.Our experiments demonstrate that STraTA can substantially improve sample efficiency across 12 fewshot benchmarks.Remarkably, on the SST-2 sentiment dataset, STraTA, with only 8 training examples per class, achieves comparable results to standard fine-tuning with 67K training examples.Our analyses reveal that task augmentation and self-training are both complementary and independently effective.
Quantum anonymous veto: a set of new protocols

Abstract We propose a set of protocols for quantum anonymous veto (QAV) broadly categorized under the probabilistic, iterative, and deterministic schemes. The schemes are based upon different types of quantum resources. Specifically, they may be viewed as single photon-based, bipartite and multipartite entangled states-based, orthogonal state-based and conjugate coding-based. The set of the proposed schemes is analyzed for all the requirements of a valid QAV scheme (e.g., privacy, verifiability, robustness, binding, eligibility and correctness). The proposed schemes are observed to be more efficient in comparison to the existing QAV schemes and robust up to the moderate decoherence rate. In addition, a trade-off between correctness and robustness of the probabilistic QAV schemes is observed. Further, the multipartite dense coding based deterministic QAV scheme is most efficient scheme among the set of schemes proposed here. A bipartite entanglement based iterative scheme employing dense coding is yet another efficient and practical scheme. The intrinsic connections between dining cryptographer-net with anonymous veto-net is also explored in the process of designing new protocols.
An Autonomous Driving Framework for Long-Term Decision-Making and Short-Term Trajectory Planning on Frenet Space

In this paper, we present a hierarchical framework for decision-making and planning on highway driving tasks. We utilized intelligent driving models (IDM and MOBIL) to generate long-term decisions based on the traffic situation flowing around the ego. The decisions both maximize ego performance while respecting other vehicles&#x0027; objectives. Short-term trajectory optimization is performed on the Frenet space to make the calculations invariant to the road&#x0027;s three-dimensional curvatures. A novel obstacle avoidance approach is introduced on the Frenet frame for the moving obstacles. The optimization explores the driving corridors to generate spatiotemporal polynomial trajectories to navigate through the traffic safely and obey the BP commands. The framework also introduces a heuristic supervisor that identifies unexpected situations and recalculates each module in case of a potential emergency. Experiments in CARLA simulation have shown the potential and the scalability of the framework in implementing various driving styles that match human behavior.
Smart campus teaching system based on ZigBee wireless sensor network

In order to build a smart campus teaching system with good teaching efficiency, this paper improves the traditional ZigBee algorithm and builds a smart campus teaching system based on ZigBee wireless sensors. Moreover, this paper improves the estimation algorithm of students' specific location and direction on campus, and analyzes the network structure of the smart teaching system based on the actual needs of smart teaching, and builds a three-tier teaching structure system. In addition, on the basis of demand analysis, this paper sets the system function modules, analyzes the data processing effect of the network nodes of this smart campus through experimental research, and evaluates the system teaching effect. Finally, this paper uses simulation technology to analyze 100 sets of data to evaluate the data processing effect of ZigBee network. Through the experimental research results, we can see that the smart campus teaching system constructed in this paper has good results.
Chatbot-Based Application Development and Implementation as an Autonomous Language Learning Medium

Autonomous learning has an important role in online learning because teachers cannot directly supervise the student learning process, which makes students themselves responsible for their learning. As an attempt to provide teachers with an alternative autonomous learning medium, this study aimed to use a chatbot-based Japanese grammar learning application namely Gengobot as an autonomous Japanese learning medium. This study applied Analysis, Design, Development, Implementation, and Evaluation (ADDIE) instructional design model. The data of this research was collected using a pre-experimental method and distributed a Likert scale questionnaire to 50 Japanese language learners with Japanese language levels equivalent to or less than JLPT level N3. The results showed that Gengobot as a chatbot-based Japanese grammar learning medium is an interesting and innovative medium to support Japanese autonomous learning because learners can decide how they learn using this application to improve their Japanese grammar skills. In addition, Gengobot is a chatbot-based learning medium that is more interactive than other Mobile-based media, which makes learners more interested in using Gengobot as a Japanese grammar learning medium. However, Gengobot still needs further development such as adding advanced grammar content (N2 and N1), adding Japanese pronunciation features (audio), etc.
“My haters and I ”: personal and political responses to hate speech against female journalists in Austria

Journalists are consistent targets of hate speech, which aims at silencing critical voices; yet female journalists encounter forms of hate speech that are unique to their gender. Hate speech against female journalists can have far-reaching implications on journalism practice and society as a whole. This study investigates the macro and micro dimensions of countering hate speech as a matter of policy and as a matter of personal resistance. Its first part analyses the legal and institutional means to counter hate speech in Austria while the second part juxtaposes the micro level of experiencing hate speech and forms of resistance through in-depth interviews with nine female journalists in Austria. The findings indicate that female journalists received more hate speech when they spoke about stereotypically male-dominated topics. They noted that receiving hate speech could have a severe impact on one's personal life or work. As measures of countering those effects, female journalists responded by making the hate comments public, while others withdrew themselves from public altogether. Deterred from seeking support from the authorities, as they did not feel taken seriously, women turned to other women who showed solidarity. The paper concludes with suggestions of practical action deriving from both sets of information.
CNN-based Fast Split Mode Decision Algorithm for Versatile Video Coding (VVC) Inter Prediction

Versatile Video Coding (VVC) is the latest video coding standard developed by Joint Video Exploration Team (JVET).In VVC, the quadtree plus multi-type tree (QT+MTT) structure of coding unit (CU) partition is adopted, and its computational complexity is considerably high due to the brute-force search for recursive rate-distortion (RD) optimization.In this paper, we aim to reduce the time complexity of inter-picture prediction mode since the inter prediction accounts for a large portion of the total encoding time.The problem can be defined as classifying the split mode of each CU.To classify the split mode effectively, a novel convolutional neural network (CNN) called multi-level tree (MLT-CNN) architecture is introduced.For boosting classification performance, we utilize additional information including inter-picture information while training the CNN.The overall algorithm including the MLT-CNN inference process is implemented on VVC Test Model (VTM) 11.0.The CUs of size 128×128 can be the inputs of the CNN.The sequences are encoded at the random access (RA) configuration with five QP values {22, 27, 32, 37, 42}.The experimental results show that the proposed algorithm can reduce the computational complexity by 11.53% on average, and 26.14% for the maximum with an average 1.01% of the increase in Bjøntegaard delta bit rate (BDBR).Especially, the proposed method shows higher performance on the sequences of the A and B classes, reducing 9.81%~26.14% of encoding time with 0.95%~3.28% of the BDBR increase.