Reviewerly/exHarmony huggingface dataset:
An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition

We propose an ensemble learning framework with Poisson sub-sampling to effectively train a collection of teacher models to issue some differential privacy (DP) guarantee for training data. Through boosting under DP, a student model derived from the training data suffers little model degradation from the models trained with no privacy protection. Our proposed solution leverages upon two mechanisms, namely: (i) a privacy budget amplification via Poisson sub-sampling to train a target prediction model that requires less noise to achieve a same level of privacy budget, and (ii) a combination of the sub-sampling technique and an ensemble teacher-student learning framework that introduces DP-preserving noise at the output of the teacher models and transfers DP-preserving properties via noisy labels. Privacy-preserving student models are then trained with the noisy labels to learn the knowledge with DP-protection from the teacher model ensemble. Experimental evidences on spoken command recognition and continuous speech recognition of Mandarin speech show that our proposed framework greatly outperforms existing state-of-the-art DP-preserving algorithms in both ASR tasks.
Piecewise-potential-field-based path planning method for fixed-wing UAV formation

The multi-UAV path planning method based on artificial potential field (APF) has the advantage of rapid processing speed and the ability to deal with dynamic obstacles, though some problems remain-such as a lack of consideration of the initial heading constraint of the UAVs, making it easy to fall into a local minimum trap, and the path not being sufficiently smooth. Consequently, a fixed-wing UAV formation path planning method based on piecewise potential field (PPF) is proposed, where the problem of UAV formation flight path planning in different states can be solved by suitable design of the PPF function. Firstly, the potential field vector can be used to represent the potential field functions of obstacles and target points to meet the kinematic constraints of the UAV. Secondly, the local minimum region can be detected, the additional potential field vector being set to break away from this region. Finally, the change rules of the potential field vector of a UAV in the formation reconstruction scene can be designed, a smooth formation flight track being assured by adjusting the corresponding speed of each UAV track point. Considering the path planning of a five-UAV formation as an example, we conducted simulation experiments. The results showed that-compared with the existing methods based on APF-the results obtained using the PPF-based method considered the initial heading limits of the UAVs, the planned path being considerably smoother. Moreover, the proposed method could plan multiple UAV tracks, satisfying the known constraints without conflict in complex scenarios.
A NEW APPROACH FOR MAPPING LAND USE / LAND COVER USING GOOGLE EARTH ENGINE: A COMPARISON OF COMPOSITION IMAGES

Abstract. In view of the increase in human activities, climate change and related hazards, land use and land cover (LULC) mapping is becoming a fundamental part of the process of any development or hazard prevention project. From this perspective, we propose a new approach for mapping LULC using Machine learning algorithms by comparing the result of five composition methods based on Google Earth Engine in the city of Tetouan - Morocco. To achieve this goal, considering the Sentinel S2 L2 imageries as a source data , five datasets were derived to make the classification generating by aggregating functions (median , mean , max , min and mode). Then based on the very high resolution (VHR) satellite images provided by Google Earth comes the next step that involves selecting samples that are divided into five classes (barren land, water surface, vegetation, forest, and urban areas), which will be further split into two parts: 70% as a training data -used to feed the machine learning algorithms (support vector machine (SVM), random forest (RF) and classification and regression trees (CART))- and 30% as a testing data for evaluating the models using accuracy assessments. The results for all datasets indicate that the SVM algorithm has the highest accuracy and its performance is better than the other algorithms (RF and CART). The average overall accuracy of SVM , RF , and CART was 87.99% , 87.81% and 84.72% , respectively. Furthermore, for each algorithm, the comparison between the results of the different composites indicates that the use of the mean composite is the most suitable for LULC mapping. Finally, GEE has proven to be an effective and rapid method for LULC mapping, especially with the use of compositional imagery that can assist decision makers in future planning or risk prevention.
A Federated Learning Approach With Imperfect Labels in LoRa-Based Transportation Systems

Intelligent Transportation System (ITS) helps to improve vehicle health, driver safety, and passenger comfort. Remotely sharing the information of ITS to train the machine and deep learning models hamper data privacy and generate security threats to the passenger, driver, and vehicle owners. Moreover, sharing the information requires huge networking resources such as high data rate, low latency, and low packet loss. Federated learning provides privacy-preserving model training on the vehicle without sharing the information. However, due to poor annotation mechanisms, federated learning may suffer from imperfect labels. This paper proposes a federated learning approach for ITS that can handle imperfect labels in the datasets of the participants. The approach also uses a Long-Range network to provide communication efficient connectivity. The approach initially estimates class-wise centroids of the datasets at the participants and server and then identifies participants with imperfect labels using similarity scores. Such participants demand the fraction of the correctly annotated dataset at the server to improve performance. We further derive the expression for the optimal fraction of the dataset requested by a participant. We finally verify the effectiveness of the proposed approach using the existing model and publicly available dataset.
Needs Analysis to Design an English Blended Learning Program: Teachers’ and Administrators’ Voices

This needs analysis study sought to identify the contextual requirements to design and implement a blended learning program in English at a Colombian public university. Data on teachers’ and administrators’ perceptions were gathered through a questionnaire, interviews, and focus groups and analyzed using grounded theory. Findings revealed the need to invest considerably in new personnel and e-infrastructure. Likewise, students’ context should be considered to design EFL blended programs. Teachers and students should be offered ICT and methodological professional development. Finally, the program should carefully balance the integration of face-to-face and online modalities. This investigation can help the academic community of language educators and curriculum designers carry out needs analysis studies for creating contextualized blended learning programs.
Number-phase uncertainty relations and bipartite entanglement detection in spin ensembles

We present a method to detect bipartite entanglement based on number-phase-like uncertainty relations in split spin ensembles. First, we derive an uncertainty relation that plays the role of a number-phase uncertainty for spin systems. It is important that the relation is given with well-defined and easily measurable quantities, and that it does not need assuming infinite dimensional systems. Based on this uncertainty relation, we show how to detect bipartite entanglement in an unpolarized Dicke state of many spin-1/2 particles. The particles are split into two subensembles, then collective angular momentum measurements are carried out locally on the two parts. First, we present a bipartite Einstein-Podolsky-Rosen (EPR) steering criterion. Then, we present an entanglement condition that can detect bipartite entanglement in such systems. We demonstrate the utility of the criteria by applying them to a recent experiment given in K. Lange et al. [Science 360, 416 (2018)] realizing a Dicke state in a Bose-Einstein condensate of cold atoms, in which the two subensembles were spatially separated from each other. Our methods also work well if split spin-squeezed states are considered. We show in a comprehensive way how to handle experimental imperfections, such as the nonzero particle number variance including the partition noise, and the fact that, while ideally BECs occupy a single spatial mode, in practice the population of other spatial modes cannot be fully suppressed.
Multi-Head Self-Attention Gated-Dilated Convolutional Neural Network for Word Sense Disambiguation

Word sense disambiguation (WSD) is to determine correct sense of ambiguous word based on its context. WSD is widely used in text classification, machine translation and information retrieval and so on. In order to improve accuracy of simplified Chinese WSD, a WSD model based on multi-head self-attention and gated-dilated convolutional neural network(AGDCNN) is proposed. Ambiguous word is viewed as the center and 4 adjacent lexical units are extracted successively toward the left and right side. Words, parts of speech, and semantic categories in 4 adjacent lexical units are vectorized and the vectorized results are input into gated-dilated convolutional neural network to get discriminative features. Then, multi-head self-attention is adopted to learn the difference and connection among discriminative features fully. Finally, classification weights are output from adaptive average pooling layer. Experiments are conducted on SemEval-2007: Task#5 and SemEval-2021: Task#2. Experimental results show that AGDCNN model has higher accuracy compared with other methods. Our goal is to improve the quality of simplified Chinese WSD as much as possible based on current linguistic resources and machine learning methods. The challenge we face is to extract effective discriminative features and design disambiguation model in high quality. Our novelty lies in that gated-dilated convolution is combined with multi-head self-attention to extract effective discriminative features, and learn their difference and connection from word form, parts of speech, and semantic categories.
Convolutional Neural Network Bootstrapped by Dynamic Segmentation and Stigmergy-Based Encoding for Real-Time Human Activity Recognition in Smart Homes

Recently, deep learning (DL) approaches have been extensively employed to recognize human activities in smart buildings, which greatly broaden the scope of applications in this field. Convolutional neural networks (CNN), well known for feature extraction and activity classification, have been applied for estimating human activities. However, most CNN-based techniques usually focus on divided sequences associated to activities, since many real-world employments require information about human activities in real time. In this work, an online human activity recognition (HAR) framework on streaming sensor is proposed. The methodology incorporates real-time dynamic segmentation, stigmergy-based encoding, and classification with a CNN2D. Dynamic segmentation decides if two succeeding events belong to the same activity segment or not. Then, because a CNN2D requires a multi-dimensional format in input, stigmergic track encoding is adopted to build encoded features in a multi-dimensional format. It adopts the directed weighted network (DWN) that takes into account the human spatio-temporal tracks with a requirement of overlapping activities. It represents a matrix that describes an activity segment. Once the DWN for each activity segment is determined, a CNN2D with a DWN in input is adopted to classify activities. The proposed approach is applied to a real case study: the “Aruba” dataset from the CASAS database.
Large-Scale Knowledge Synthesis and Complex Information Retrieval from Biomedical Documents

Recent advances in the healthcare industry have led to an abundance of unstructured data, making it challenging to perform tasks such as efficient and accurate information retrieval at scale. Our work offers an all-in-one scalable solution for extracting and exploring complex information from large-scale research documents, which would otherwise be tedious. First, we briefly explain our knowledge synthesis process to extract helpful information from unstructured text data of research documents. Then, on top of the knowledge extracted from the documents, we perform complex information retrieval using three major components- Paragraph Retrieval, Triplet Retrieval from Knowledge Graphs, and Complex Question Answering (QA). These components combine lexical and semantic-based methods to retrieve paragraphs and triplets and perform faceted refinement for filtering these search results. The complexity of biomedical queries and documents necessitates using a QA system capable of handling queries more complex than factoid queries, which we evaluate qualitatively on the COVID-19 Open Research Dataset (CORD-19) to demonstrate the effectiveness and value-add.
Sentiment Analysis of COVID-19 Vaccines in Indonesia on Twitter Using Pre-Trained and Self-Training Word Embeddings

Sentiment analysis regarding the COVID-19 vaccine can be obtained from social media because users usually express their opinions through social media. One of the social media that is most often used by Indonesian people to express their opinion is Twitter. The method used in this research is Bidirectional LSTM which will be combined with word embedding. In this study, fastText and GloVe were tested as word embedding. We created 8 test scenarios to inspect performance of the word embeddings, using both pre-trained and self-trained word embedding vectors. Dataset gathered from Twitter was prepared as stemmed dataset and unstemmed dataset. The highest accuracy from GloVe scenario group was generated by model which used self-trained GloVe and trained on unstemmed dataset. The accuracy reached 92.5%. On the other hand, the highest accuracy from fastText scenario group generated by model which used self-trained fastText and trained on stemmed dataset. The accuracy reached 92.3%. In other scenarios that used pre-trained embedding vector, the accuracy was quite lower than scenarios that used self-trained embedding vector, because the pre-trained embedding data was trained using the Wikipedia corpus which contains standard and well-structured language while the dataset used in this study came from Twitter which contains non-standard sentences. Even though the dataset was processed using stemming and slang words dictionary, the pre-trained embedding still can not recognize several words from our dataset.
STUDI LITERATUR UPAYA MENINGKATKAN LITERASI INFORMASI SISWA PADA MATA PELAJARAN BIOLOGI SELAMA BELAJAR DARING EFEK COVID-19

This study aims to find out more about the efforts to improve students' information literacy in online learning of the covid-19 effect of biology. The type of research used in this paper is a literature review study. This research was carried out by analyzing research related to how to increase information literacy in online learning the effects of Covid-19 describing, evaluating and clarifying the research and then drawing conclusions. The result of this research is that information literacy is a very important skill for students. At this time of online learning, students who have good information literacy skills will have no difficulty in learning. However, there are problems with students that it is difficult to study at home. Even though the average high school student is able to use existing technology. Therefore, efforts are needed to improve student information literacy. The conclusion in this study, based on the results of the author's analysis, there are three ways to improve students' information literacy, the methods are 1) doing biology online learning effectively to increase student interest in learning and foster student curiosity, 2) the teacher provides many learning resources. as reading material in learning and increasing student insight, and 3) parents and teachers must work together in monitoring, facilitating, and motivating students to develop their learning patterns, so that learning takes place effectively and can improve student�s information literacy.
Analisis Prediktif Pemeliharaan Minyak Transformator Menggunakan Metode Markov

As one of the main assets that play an important role in the distribution of electricity, the performance of the transformer must be maintained in prime condition. Damage to the transformer insulation oil often causes failure of the transformer. Damage to transformer oil is very difficult to predict, therefore monitoring its condition is very important to do. Dissolved Gas Analysis (DGA) is one og the method to determine the condition of transformer oil, where this method can analyze the condition of the transformer based on the concentration of gas dissolved in oil. A reguler maintenance of transformer oil can prevent the transformer oil from damage or failure of insulation. However, sometimes maintenance is carried out not in a right time that results in a decrease in transformer’s lifetime. In this analysis the value of Total Dissolved Combustible Gas (TDCG) was used to calculate transfomer’s reliability and availability value. Those value then processed using the Markov method to determine the possibilities of proper maintenance time. From the calculation results in this analysis the transformer reliability value in 30 days is 0.886, then the probability of decreasing the reliability value every month is 11.4%. And the value of the availability of the transformer is 0.803 or equal to 293 days. Based on this analysis, in one year the transformer operated properly for 293 days while 72 days the transformer was in an unfavorable condition. So that maintenance should be carried out in 293 days transformer’s condition can be maintained properly.
Edge device for movement pattern classification using neural network algorithms

Portable electronic systems allow the analysis and monitoring of continuous time signals, such as human activity, integrating deep learning techniques with cloud computing, causing network traffic and high energy consumption. In addition, the use of algorithms based on neural networks are a very widespread solution in these applications, but they have a high computational cost, not suitable for edge devices. In this context, solutions are created that bring data analysis closer to the edge of the network, so in this paper models adapted to an edge device for the recognition of human activity are evaluated, considering characteristics such as inference time, memory, and precision. Two categories of models based on deep and convolutional neural networks are developed by implementing them in C language and comparing with the TensorFlow Lite platform. The results show that the implementations with libraries have a better accuracy result of 76% using principal component analysis inputs, obtaining an execution time of 9ms. Therefore, when evaluating the models, we must not only consider their accuracy but also the execution time and memory on the device.
GIT: A Generative Image-to-text Transformer for Vision and Language

In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering. While generative models provide a consistent network architecture between pre-training and fine-tuning, existing work typically contains complex structures (uni/multi-modal encoder/decoder) and depends on external modules such as object detectors/taggers and optical character recognition (OCR). In GIT, we simplify the architecture as one image encoder and one text decoder under a single language modeling task. We also scale up the pre-training data and the model size to boost the model performance. Without bells and whistles, our GIT establishes new state of the arts on 12 challenging benchmarks with a large margin. For instance, our model surpasses the human performance for the first time on TextCaps (138.2 vs. 125.5 in CIDEr). Furthermore, we present a new scheme of generation-based image classification and scene text recognition, achieving decent performance on standard benchmarks. Codes are released at \url{https://github.com/microsoft/GenerativeImage2Text}.
An Assessment of YOLO Architectures for Oil Tank Detection from SPOT Imagery

Since it can be used to manage and estimate oil reserves, the inventory of oil tanks is essential for both the economy and the military applications. Considering oil tanks contain valuable materials required for transportation and industrial production, they are a significant type of target. Oil tank detection techniques have several uses, including monitoring disasters, preventing oil leaks, designing cities, and assessing damage. Huge amount of satellite imagery has recently been available and it is used in both the military and civil applications. The new spaceborne sensors' higher resolution enables the detection of targeted objects. Therefore, remote sensing instruments provide ideal tools for oil tank detection task. Conventional approaches for oil tank detection from high resolution remote sensing imagery generally relies on geometric shape, structure, contract differences and color information of the boundary or hand-crafted features. However, these methods come along with vulnerabilities and hence it can be challenging to obtain accurate detection in the presence of a number of disturbance elements, particularly a wide range of colours, size variations, and the shadows that view angle and illumination create. Therefore, deep learning-based methods can provide a big advantage for solution of this task. In this regard, this study employs four YOLO models namely YOLOv5, YOLOX, YOLOv6 and YOLOv7 for oil tank detection from high-resolution optical imagery. Our results show that YOLOv7 and YOLOv5 architectures provide more accurate detections with mean average precision values of 68.11% and 69.69%, respectively. The experiments and visual inspections reveal efficiency, generalization and transferability of these models.