Reviewerly/exHarmony huggingface dataset:
An Automatic Exposure Method of Plane Array Remote Sensing Image Based on Two-Dimensional Entropy

The improper setting of exposure time for the space camera will cause serious image quality degradation (overexposure or underexposure) in the imaging process. In order to solve the problem of insufficient utilization of the camera’s dynamic range to obtain high-quality original images, an automatic exposure method for plane array remote sensing images based on two-dimensional entropy is proposed. First, a two-dimensional entropy-based image exposure quality evaluation model is proposed. The two-dimensional entropy matrix of the image is partitioned to distinguish the saturated areas (region of overexposure and underexposure) and the unsaturated areas (region of propitious exposure) from the original image. The ratio of the saturated area is used as an evaluating indicator of image exposure quality, which is more sensitive to the brightness, edges, information volume, and signal-to-noise ratio of the image. Then, the cubic spline interpolation method is applied to fit the exposure quality curve to efficiently improve the camera’s exposure accuracy. A series of experiments have been carried out for different targets in different environments using the existing imaging system to verify the superiority and robustness of the proposed method. Compared with the conventional automatic exposure method, the signal-to-noise ratio of the image obtained by the proposed algorithm is increased by at least 1.6730 dB, and the number of saturated pixels is reduced to at least 2.568%. The method is significant to improve the on-orbit autonomous operating capability and on-orbit application efficiency of space camera.
Light regression memory and multi-perspective object special proposals for abrupt motion tracking

Visual tracking is challenging as the tracked target often undergo drastic appearance changes, fast motion, out-of-view and other abrupt motion in real world. Also, how to monitor the tracking failure in real-time and how to recover after tracking failure detected are very attractive research problems. In this paper, we propose the abrupt motion tracking method via the light regression memory network and multi-perspective object special proposals to resolve these problems. Firstly, we propose a highly accurate multi-perspective object special proposal method to recover tracking when tracking failure happened. Commonsense information is used as a priori knowledge to refine region proposals. Meanwhile, semantic target-aware estimation and spatial structure estimation are designed to rank and choose region proposals from two complementary viewpoints separately. By a comprehensive decision, the final proposals can fully cover the target’s states even under abrupt motion. Secondly, we learn the memory network on a single-convolutional layer network by convolution linear regression. Without training on large offline datasets, it only need to fine-tune a fixed number of network parameters online by reliable target frame information to reinforce the memory of the changes in the target’s appearance. Without size increasing and complex update strategy, the memory network can keep the good pattern of the target to reliably determine whether tracking fails, which ensures the effectivity of model updating and locating the target well. Finally, we integrate the memory network and region proposals into Siamese matching framework to achieve accurate abrupt motion tracking. Numerous experiments over multiple tracking benchmarks prove that the proposed tracker achieves excellent performance.
Poetics of Future Work: Blending Speculative Design with Artistic Methodology

This case study presents how the mixing of speculative design with artistic methodology can contribute to the inquiry of technological potentialities in the future of work. The goal and belief are that technologies such as artificial intelligence can augment employee creativity and support their well-being at work. The co-design process followed an artistic approach and consisted of three cycles of labs, workshops and events during the span of one year to support professionals with non-technical background in the ideation and conceptualization of possible futures. The artistic approach consisted of different exploration perspectives of technology through the use of embodiment, artifacts and creation of speculative fictions. The research team that facilitated the labs was interdisciplinary and the participants were assembled from different partner organizations from industry and public sector. We share the learnings from this study attending to three different perspectives: our learnings from the facilitation of the artistic approach, our learnings from the future of work ideas and concepts developed by participants, and discussion of what these learnings can mean to design practitioners and the research community. Results indicate that embodiment and speculative fiction can create engagement among professionals that lack technical expertise and support them in collaborative exploration of alternative futures of work with novel and abstract technologies such as AI.
Efficient pyramid context encoding and feature embedding for semantic segmentation

For reality applications of semantic segmentation, inference speed and memory usage are two important factors. To address these challenges, we propose a lightweight feature pyramid encoding network (FPENet) for semantic segmentation with a good trade-off between accuracy and speed. We use a series of feature pyramid encoding (FPE) blocks to encode context at multiple scales in the encoder. Each FPE block consists of different depthwise dilated convolutions that perform as a spatial pyramid to extract features and reduce computational costs. During training, a one-shot neural architecture search algorithm is adopted to find the optimal structure for each FPE block from a large search space with a small search cost. After the search for the encoder, a mutual embedding upsample module is introduced in the decoder, consisting of two attention blocks. The encoder-decoder attention mechanism is used to help aggregate efficiently high-level semantic features and low-level spatial details. The proposed network outperforms the existing real-time methods with fewer parameters and improved inference speed on the Cityscapes and CamVid benchmark datasets. Specifically, it achieved 72.3% mean IoU on the Cityscapes test set with only 0.4 M parameters and 192.6 FPS speed on an Nvidia Titan V100 GPU, and 73.4% mean IoU with 116.2 FPS when running on higher resolution images.
Deep Superpixel Cut for Unsupervised Image Segmentation

Image segmentation, one of the most critical vision tasks, has been studied for many years. Most of the early algorithms are unsupervised methods, which use hand-crafted features to divide the image into many regions. Recently, owing to the great success of deep learning technology, CNNs based methods show superior performance in image segmentation. However, these methods rely on a large number of human annotations, which are expensive to collect. In this paper, we propose a deep unsupervised method for image segmentation, which contains the following two stages. First, a Superpixelwise Autoencoder (SuperAE) is designed to learn the deep embedding and reconstruct a smoothed image, then the smoothed image is passed to generate superpixels. Second, we present a novel clustering algorithm called Deep Superpixel Cut (DSC), which measures the deep similarity between superpixels and formulates image segmentation as a soft partitioning problem. Via backpropagation, DSC adaptively partitions the superpixels into perceptual regions. Experimental results on the BSDS500 dataset demonstrate the effectiveness of the proposed method.
Chinese Sentiment Analysis Based on Lightweight Character-Level BERT

With the emergence of more and more deep learning application scenarios, training a complete model from scratch in various scenarios is a tedious and costly task, so transfer learning has received more and more attention. We all know that sentiment analysis as an effective means of acquiring user behavior has been widely used in various social or e-commerce platforms in the field of natural language processing. However, the traditional Chinese sentiment classification model is constructed based on word vectors due to the limitation of the language characteristics of Chinese itself, so it is necessary to do some preprocessing of word segmentation in advance. The performance of the word segmentation directly affects the performance of the model and training a complete model from scratch requires sufficient computing resources. In this paper, we propose a character level transfer learning model built on a lightweight pre-trained BERT model, here we named it CTBERT. On the basis of fully absorbing the attention mechanism of Transformer, we fine-tuned and trained CTBERT, and then compare CTBERT with traditional machine learning and deep learning models. The results show that our transfer model has a significant performance improvement over existing model.
My-Covid-Safe-Town: A mobile application to support post-Covid recovery of small local businesses

The Covid-19 pandemic growth has led to a large desire for safety restrictions among citizens near or in Covid-19 affected areas. This includes requiring the use of masks when outdoors and an occupancy limit being placed when indoors. Some of these restrictions have been enforced by the government and can lead to infraction charges on those who choose to ignore them, but some restrictions are up to the decision of the respective individuals. This has led to varying levels of safety being applied when outside. This is especially concerning when some businesses may not be taking proper precautions to avoid the spread of Covid-19. To counterbalance this issue while also spreading awareness of the businesses that are careful enough to follow such precautions, the app My-Covid-Safe-Town (MCST) is created. MCST allows for individuals (i.e., patrons) to find businesses that fit the standard of safety and to specifically point out the steps being taken by each business to avoid the spread of the pandemic.
Crafting Adversarial Samples for Anomaly Detectors in Industrial Control Systems

The increasing adoption of the Industry 4.0 paradigm encompasses digitally interconnected factories which enables many advantages. However, it is still necessary to dedicate effort towards investigating protection mechanisms against cyberattacks in these scenarios. Despite the power demonstrated by Anomaly Detection-based Intrusion Detection Systems in industrial scenarios, their vulnerabilities to adversarial attacks, especially to evasion attacks, make Machine Learning and Deep Learning models ineffective for real scenarios. These type of attacks craft samples misclassified by the Intrusion Detection System and potentially reach industrial devices, causing potentially damaging impacts to factory workers and industry resources. Adversarial attacks linked to industrial scenarios are currently in early stages of development, hence most of them have the capability to craft samples misclassified by the IDS but not reach industrial devices. In this work, we present a new adversarial attack named Selective and Iterative Gradient Sign Method that overcomes the limitation of the adversarial attacks present in the literature. To complement this work we also detail a study of how the detection rate of an Intrusion Detection System is degraded and the time required by each technique to generate adversarial samples. The experiments were carried out using a dataset named Electra, collected from an Electric Traction Substation, and showed that adversarial attacks evaluated crafted samples misclassified by the IDS. However, only the method we proposed generated samples that can be understood by intermediate network devices and, therefore, reach their destination. Our experiment outputs demonstrate a lower period of time to achieve and craft adversarial samples using out our iterative based process method as opposed to other current iterative methods currently available.
Money Whispers: Informality, International Politics, and Immigration in Transnational Finance

While the growth of financial technologies (FinTech) is making the flow of money faster, easier, and more secure, such technologies are often unable to serve many countries due to the global political environment. Despite its severe impact, this issue has remained understudied in the HCI literature. We address this gap by presenting our findings from a three-month-long ethnography with the Iranian community in Toronto, Canada. We present their struggles in transferring money to and from their home country - a process that entails financial loss, fear, uncertainty, and privacy breaches. We also outline the informal workarounds that allow this community to circumvent these challenges, along with the associated hassles. This paper contributes to broadening the scope of FinTech in the HCI literature by connecting it with the politics surrounding transnational transactions. We discuss the design implications of our findings and their contribution to the broader interests of HCI in mobilities and social justice.
Scalable Privacy-Preserving Distributed Extremely Randomized Trees for Structured Data With Multiple Colluding Parties

Today, in many real-world applications of machine learning algorithms, the data is stored on multiple sources instead of at one central repository. In many such scenarios, due to privacy concerns and legal obligations, e.g., for medical data, and communication/computation overhead, for instance for large scale data, the raw data cannot be transferred to a center for analysis. Therefore, new machine learning approaches are proposed for learning from the distributed data in such settings. In this paper, we extend the distributed Extremely Randomized Trees (ERT) approach w.r.t. privacy and scalability. First, we extend distributed ERT to be resilient w.r.t. the number of colluding parties in a scalable fashion. Then, we extend the distributed ERT to improve its scalability without any major loss in classification performance. We refer to our proposed approach as k-PPD-ERT or Privacy-Preserving Distributed Extremely Randomized Trees with k colluding parties.
Language agnostic model

Social media platforms can struggle to enforce rules preventing online abuse and hate speech due to the large amount of content that must be manually reviewed. Machine learning approaches have been proposed in the literature as a way to automate much of these labors, but social content in multiple languages further complicates this issue. Past work has focused on first building word embeddings in the target language which limits the application of such embeddings to other languages. We use the Google Neural Machine Translator (NMT) to identify and translate Non-English text to English to make the system language agnostic. We can therefore use already available pre-trained word embeddings, instead of training our models and word embeddings in different languages. We have experimented with different word-embedding and classifier pairs as we aimed to assess whether translated English data gives us accuracy comparable to an untranslated English dataset. Our best performing model, SVM with TF-IDF, gave us a 10-fold accuracy of 95.56 percent followed by the BERT model with a 10-fold accuracy of 94.66 percent on the translated data. This accuracy is close to the accuracy of the untranslated English dataset and far better than the accuracy of the untranslated Hindi dataset.
Automatic generation of Web service for the Praxeme software aspect from the ReLEL requirements model

Praxeme is an enterprise methodology that combines the two approaches SOA and MDA to build an information system. It is based on the ideology of separating the business concern into a homogeneous set called "aspect" to better control the usual complexity of a system. The instantiation of the ReLEL requirements model in the Praxeme methodology is an interesting approach to improve the quality of the software development process because it not only allows to specify the user requirements but also to represent the very precise conceptual level of the future system to be designed. Therefore, this article deals with the automatic generation of SOAP web services that represent the software aspect of the Praxeme methodology from the intentional aspect specified by the ReLEL requirements model. To achieve this, we proceeded in two steps, namely the proposal of the rules for deriving the Praxeme Logical Factory Model (logical aspect) into a Web Service Description Language model or WSDL (software aspect) classified as Model to Model Transformation (M2M) in the MDA approach and then the proposal of the rules for translating the WSDL model into a WSDL document known as Model to Text Translation (M2T). The result obtained from the approach proposed in this paper is a WSDL file which is an XML language allowing the complete description of a Web service. We used ATL for the implementation of the M2M approach transformation rules and then the Acceleo template engine for the translation of the M2T.
Multi-Class Network Traffic Generators and Classifiers Based on Neural Networks

We introduce a neural network framework that allows constructing multi-class network traffic models suitable for flow generation and classification tasks. Packet size and inter-packet time sequences were the only flow features that formed the inputs. Our approach has two main components: a packet feature quantizer and a sequence model, which we verified on example configuration with K-Means algorithm and Transformer-based models respectively. Moreover, we claim a possibility of knowledge transfer from the generation to the classification task. The evaluation of the generator's traffic quality was carried out by means of Kolmogorov-Smirnov metric for such parameters as packet size, inter-arrival time, throughput and number of packets per flow in directions to and from traffic origin. The results showed that the proposed multi-class model performed on par with individual single-class Markov-based models. As for the classification task, the model outperformed FS-NET and a conventional Random Forest classifier. We demonstrated that performing the knowledge transfer procedure between traffic generator and classifier (so-called generative pretraining) could improve performance on the traffic classification task even further. We also release the code, datasets and trained models.
Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor

Fall-related information can help clinical professionals make diagnoses and plan fall prevention strategies. The information includes various characteristics of different fall phases, such as falling time and landing responses. To provide the information of different phases, this pilot study proposes an automatic multiphase identification algorithm for phase-aware fall recording systems. Seven young adults are recruited to perform the fall experiment. One inertial sensor is worn on the waist to collect the data of body movement, and a total of 525 trials are collected. The proposed multiphase identification algorithm combines machine learning techniques and fragment modification algorithm to identify pre-fall, free-fall, impact, resting and recovery phases in a fall process. Five machine learning techniques, including support vector machine, k-nearest neighbor (kNN), naïve Bayesian, decision tree and adaptive boosting, are applied to identify five phases. Fragment modification algorithm uses the rules to detect the fragment whose results are different from the neighbors. The proposed multiphase identification algorithm using the kNN technique achieves the best performance in 82.17% sensitivity, 85.74% precision, 73.51% Jaccard coefficient, and 90.28% accuracy. The results show that the proposed algorithm has the potential to provide automatic fine-grained fall information for clinical measurement and assessment.
A High Throughput Parallel Hash Table Accelerator on HBM-enabled FPGAs

Hash table is a key component in a number of AI algorithms such as Graph Convolutional Neural Networks, Approximate Nearest Neighbor Search, Bag-of-Words based Text Mining algorithms, etc. Efficient implementation of hash tables is needed for a wide range of AI applications. High bandwidth memory (HBM), which provides significantly higher memory bandwidth than traditional DDR, has recently gained popularity. In this work, we propose a high throughput parallel hash table targeting HBM-enabled FPGAs. Our design is tailored for HBM architecture, allowing flexible and balanced mapping between processing engines and HBM channels at design time given query distribution and hash table properties (key/value length, collision handling, and hash table size). We further develop a novel data organization and query flow which enable our accelerator to scale up to 16 processing engines (PEs). The proposed design supports parallel search, insert, and delete queries. Experimental results demonstrate that our hash table accelerator can achieve up to 3575 million operations per second (MOPS) for search-only queries and up to 1470 MOPS for 50%/50% distributed search/update queries on HBM-enabled FPGAs. It achieves better throughput than the state-of-the-art GPU and FPGA designs by up to 3.5× and 3.2× respectively.
From the Margins to the Centre: Defining New Mission and Vision for HCI Research in South Asia

The past two decades have seen an increase in the amount of research in the CHI community from South Asia with a focus on designing for the unique and diverse socio-cultural, political, infrastructural, and geographical background of the region. However, the studies presented to the CHI community primarily focus on working with and unpacking the regional contextual constraints (of the users and the infrastructures), thus taking a developmental stance. In this online workshop, we aim to broaden the perspective of the CHI research and community towards the contributions from the region including and beyond development, by bringing together researchers, designers, and practitioners working or are interested in working within these regions on diverse topics such as universal education, global healthcare, accessibility, sustainability, and more. Through the workshop discussion, group design activity, and brainstorming, we aim to provide a space for symbiotic knowledge sharing, and defining shared visions and missions for HCI activities in South Asia for including and moving beyond the development agenda.
MigrationAdvisor: Recommending Library Migrations from Large-Scale Open-Source Data

During software maintenance, developers may need to migrate an already in-use library to another library with similar functionalities. However, it is difficult to make the optimal migration decision with limited information, knowledge, or expertise. In this paper, we present MigrationAdvisor, an evidence-based tool to recommend library migration targets through intelligent analysis upon a large number of GitHub repositories and Java libraries. The migration advisories are provided through a search engine style web service where developers can seek migration suggestions for a specific library. We conduct systematic evaluations on the correctness of results, and evaluate the usefulness of the tool by collecting usage feedback from industry developers. Video: https://youtu.be/4I75W22TqwQ.