Reviewerly/exHarmony huggingface dataset:
A Tool for Neural Network Global Robustness Certification and Training

With the increment of interest in leveraging machine learning technology in safety-critical systems, the robustness of neural networks under external disturbance receives more and more concerns. Global robustness is a robustness property defined on the entire input domain. And a certified globally robust network can ensure its robustness on any possible network input. However, the state-of-the-art global robustness certification algorithm can only certify networks with at most several thousand neurons. In this paper, we propose the GPU-supported global robustness certification framework GROCET, which is more efficient than the previous optimization-based certification approach. Moreover, GROCET provides differentiable global robustness, which is leveraged in the training of globally robust neural networks.
Continuous Active Learning Using Pretrained Transformers

Pre-trained and fine-tuned transformer models like BERT and T5 have improved the state of the art in ad-hoc retrieval and question-answering, but not as yet in high-recall information retrieval, where the objective is to retrieve substantially all relevant documents. We investigate whether the use of transformer-based models for reranking and/or featurization can improve the Baseline Model Implementation of the TREC Total Recall Track, which represents the current state of the art for high-recall information retrieval. We also introduce CALBERT, a model that can be used to continuously fine-tune a BERT-based model based on relevance feedback.
Secure Multi-Party Computation Based Distributed Feasibility Queries – A HiGHmed Use Case

The integration of routine medical care data into research endeavors promises great value. However, access to this extra-domain data is constrained by numerous technical and legal requirements. The German Medical Informatics Initiative (MII) - initiated by the Federal Ministry of Research and Education (BMBF) - is making progress in setting up Medical Data Integration Centers to consolidate data stored in clinical primary information systems. Unfortunately, for many research questions cross-organizational data sources are required, as one organization's data is insufficient, especially in rare disease research. A first step, for research projects exploring possible multi-centric study designs, is to perform a feasibility query, i.e., a cohort size calculation transcending organizational boundaries. Existing solutions for this problem, like the previously introduced feasibility process for the MII's HiGHmed consortium, perform well for most use cases. However, there exist use cases where neither centralized data repositories, nor Trusted Third Parties are acceptable for data aggregation. Based on open standards, such as BPMN 2.0 and HL7 FHIR R4, as well as the cryptographic techniques of secure Multi-Party Computation, we introduce a fully automated, decentral feasibility query process without any central component or Trusted Third Party. The open source implementation of the proposed solution is intended as a plugin process to the HiGHmed Data Sharing Framework. The process's concept and underlying algorithms can also be used independently.
Real-world Deployment of Privacy-Enhancing Authentication System using Attribute-based Credentials

With the daily increase in digitalization and integration of the physical and digital worlds, we need to better protect users’ privacy and identity. Attribute-based Credentials (ABCs) seem to be a promising technology for this task. In this paper, we provide comprehensive analyses of the readiness, maturity, and applicability of ABCs to real-world applications. Furthermore, we introduce our Privacy-Enhancing Authentication System (PEAS), which is based on ABCs and meets all privacy requirements such as anonymity and unlinkability of the user’s activities. Besides privacy features, PEAS also provides revocation mechanisms to identify and revoke malicious users. The system is suitable for deployment in real-world scenarios and runs on a wide range of user devices (e.g., smart cards, smartphones, and wearables).
High-Performance Flexible Photonic Synapse Transistors Based on a Bulk Composite Film of Organic Semiconductors with Complementary Absorption

摘要 采用光吸收互补的聚(3-己基噻吩)(P3HT)和引达省并二噻吩-苯并噻二唑共聚物(PIDT-BT),
Uconv-Conformer: High Reduction of Input Sequence Length for End-to-End Speech Recognition

Optimization of modern ASR architectures is among the highest priority tasks since it saves many computational resources for model training and inference. The work proposes a new Uconv-Conformer architecture based on the standard Conformer model. It consistently reduces the input sequence length by 16 times, which results in speeding up the work of the intermediate layers. To solve the convergence issue connected with such a significant reduction of the time dimension, we use upsampling blocks like in the U-Net architecture to ensure the correct CTC loss calculation and stabilize network training. The Uconv-Conformer architecture appears to be not only faster in terms of training and inference speed but also shows better WER compared to the baseline Conformer. Our best Uconv-Conformer model shows 47.8% and 23.5% inference acceleration on the CPU and GPU, respectively. Relative WER reduction is 7.3% and 9.2% on LibriSpeech test_clean and test_other respectively.
Seeing oneself as a data reuser: How subjectification activates the drivers of data reuse in science

Considerable resources are being invested in strategies to facilitate the sharing of data across domains, with the aim of addressing inefficiencies and biases in scientific research and unlocking potential for science-based innovation. Still, we know too little about what determines whether scientific researchers actually make use of the unprecedented volume of data being shared. This study characterizes the factors influencing researcher data reuse in terms of their relationship to a specific research project, and introduces subjectification as the mechanism by which these influencing factors are activated. Based on our analysis of semi-structured interviews with a purposive sample of 24 data reusers and intermediaries, we find that while both project-independent and project-dependent factors may have a direct effect on a single instance of data reuse, they have an indirect effect on recurring data reuse as mediated by subjectification. We integrate our findings into a model of recurring data reuse behavior that presents subjectification as the mechanism by which influencing factors are activated in a propensity to engage in data reuse. Our findings hold scientific implications for the theorization of researcher data reuse, as well as practical implications around the role of settings for subjectification in bringing about and sustaining changes in researcher behavior.
FeedLens: Polymorphic Lenses for Personalizing Exploratory Search over Knowledge Graphs

The vast scale and open-ended nature of knowledge graphs (KGs) make exploratory search over them cognitively demanding for users. We introduce a new technique, polymorphic lenses, that improves exploratory search over a KG by obtaining new leverage from the existing preference models that KG-based systems maintain for recommending content. The approach is based on a simple but powerful observation: in a KG, preference models can be re-targeted to recommend not only entities of a single base entity type (e.g., papers in the scientific literature KG, products in an e-commerce KG), but also all other types (e.g., authors, conferences, institutions; sellers, buyers). We implement our technique in a novel system, FeedLens, which is built over Semantic Scholar, a production system for navigating the scientific literature KG. FeedLens reuses the existing preference models on Semantic Scholar—people's curated research feeds—as lenses for exploratory search. Semantic Scholar users can curate multiple feeds/lenses for different topics of interest, e.g., one for human-centered AI and another for document embeddings. Although these lenses are defined in terms of papers, FeedLens re-purposes them to also guide search over authors, institutions, venues, etc. Our system design is based on feedback from intended users via two pilot surveys (n = 17 and n = 13, respectively). We compare FeedLens and Semantic Scholar via a third (within-subjects) user study (n = 15) and find that FeedLens increases user engagement while reducing the cognitive effort required to complete a short literature review task. Our qualitative results also highlight people's preference for this more effective exploratory search experience enabled by FeedLens.
ACERVO DE PERGUNTAS PARA INVESTIGAR OS IMPACTOS DA COVID-19 NOS DIREITOS HUMANOS: BANCO DE PERGUNTAS DHC-19

Despite the rich production of literature on Human Rights and their perceptions, the lack of consensus on a concept of Human Rights hinders the comparability of the various studies produced.To enable the comparability of different studies on the same theme, the reuse of questions is a widely used strategy.In this article, the authors present a database of questions open to the consultation and use of researchers and other interested parties.
Emotional state recognition from text data using machine learning and deep learning algorithm

Abstract Emotional state recognition is a process to identify user's feelings and emotions for various purposes. Emotional state examination from text comprises of extricating data about feelings, opinions, and even feelings passed on by scholars toward subjects of interest. Web‐based media is producing a tremendous measure of assessment rich information as remarks, notices, blog entries, and so forth. It is trying to comprehend the most recent patterns and rundowns the state or general feelings about items because of the enormous variety and size of web‐based media information, and this makes the need of computerized and ongoing conclusion extraction and mining. Sentiment analysis is difficult because of the existence of bad or abusive language with misspellings words. One of the major natural language processing research area is inclined toward understanding human emotions. Emotional state analysis acts like an amazing treasure and powerful tool, which renders its service to the field of deep learning. It can help service providers to fetch the requisite information to collect and identify the sentiments of the database. Principle issues that exist in the current procedures are: powerlessness to perform well in various areas, deficient exactness and execution in assessment examination dependent on lacking named information, inadequacy to manage complex sentences that require more than emotional words and basic examining. It is as yet hard for a greater part of instruments to decisively assess what genuinely is a negative, unbiased and a positive articulation. It is not advanced enough to successfully deal with sarcasm or context. So there is requirement to develop a machine learning algorithm to analyze the text data and give more better and accurate results.
PEMEROLEHAN BAHASA ANAK MELALUI PENGUKURAN MEAN LENGTH OF UTTERENCE

This research was conducted when the children were 3 years and 6 months old. The child’s nams is Muhammad Habibie. The son of Mr. Azwal Effendi and Mrs. Rostiana. This study was conducted to test the children's speaking ability in 1, 2, 3, 4 and 5 words. This study uses the average utterance length (MLU) survey method. The data collection technique was done by recording the children's spoken words using a mobile phone. The use is a tap recording method, and the recording results are written and analyzed. A method for viewing and analyzing morphemes and structures used by children. Research shows that children have poor language skills. From this the author concludes that the child cannot speak yet.
Media, Consumerism and the Environment: The Relationship of Media and Consumptive Behavior of Urban Society to Environmental Damage

In a few decades, the media has become the locomotive that controls the consumptive behavior of society, especially in urban areas. Massive media that aggressively offers easy access to meet all kinds of urban community needs encourages consumerism. The trend of consumerist urban lifestyle in the end must take consequences for environmental damage. As with Fast Food and Fashion which results in increased industrial production, the energy required for production is higher. At the same time, the energy used also comes from the exploitation of the earth. Scholars such as Jean Baudrillard suggest that people do not buy goods, but buy signs that ultimately symbolize themselves to confirm which group they belong to and belong to. To contribute to the existing studies, the study in this article aims to explore and analyze the relation of media to the consumptive behavior of urban society and its impact on environmental damage. By using the perspective of Jean Baudrillard, the media is the most significant contributor that encourages consumerism in urban communities so that it has an impact on environmental damage. The reality of the media has created a false consciousness so that consumer society only consumes signs, not goods and services. The emergence of visual waste impacts urban society's consumerism, resulting in the loss of public space and the strengthening of the greenhouse gas effect.
Enhancing the Transferability of Adversarial Examples with Feature Transformation

The transferability of adversarial examples allows the attacker to fool deep neural networks (DNNs) without knowing any information about the target models. The current input transformation-based method generates adversarial examples by transforming the image in the input space, which implicitly integrates a set of models by concatenating image transformation into the trained model. However, the input transformation-based methods ignore the manifold embedding and hardly extract intrinsic information from high-dimensional data. To this end, we propose a novel feature transformation-based method (FTM), which conducts feature transformation in the feature space. FTM can improve the robustness of adversarial example by transforming the features of data. Combining with FTM, the intrinsic features of adversarial examples are extracted to generate transferable adversarial examples. The experimental results on two benchmark datasets show that FTM could effectively improve the attack success rate (ASR) of the state-of-the-art (SOTA) methods. FTM improves the attack success rate of the Scale-Invariant Method on Inception_v3 from 62.6% to 75.1% on ImageNet, which is a large margin of 12.5%.
The AUV-Follower Control System Based on the Prediction of the AUV-Leader Movement Using Data from the Onboard Video Camera

The paper proposes a new method for the synthesis of spatial motion control systems of the AUV-leader and a group of AUV-followers during their cooperative movement in a desired formation. This system allows the provision of an accurate positioning of the followers relative to the leader using information received with a low frequency only from their onboard video cameras. To improve the accuracy of the created system it proposes a method of estimation of the parameters of the movement of the AUV-leader (its speeds and accelerations) to predict its movement relative to the AUV-follower in the time intervals between the updates of information received from video cameras.
Semantic Segmentation Optimized for Low Compute Embedded Devices

Deployment of a deep convolutional neural network on a low compute device is an increasingly important area of research. Wearable systems, drones, and robots use semantic information to manipulate, control, and estimate variables related to efficient navigation or gain of contextual information. However, real-time semantic segmentation is challenging on low compute devices. We propose a compact convolutional neural network for real-time applications on a low compute device. Our decoder uses pixel shuffling to achieve efficient inferences. We compared our CNN with state-of-the-art models ranked on the Cityscapes real-time semantic segmentation category. We propose a modified Net score that includes frame per seconds as an additional metric along with traditional metrics mIoU, GFLOPs, and the number of parameters to evaluate mobile computing performance. Our CNN achieved 65.7 FPS, 76.7% mIoU without ImageNet pre-training, while requiring 25 GFLOPs and 4.55M parameters, resulting in a 127.53 on the modified Net score compared to 119.89 for DDRNET23&#x005F;slim and 115.39 for Regseg. In addition to Cityscapes results, our CNN performance (83.3% mIoU and 354 FPS with TensorRT) was superior to published mIoU values for Regseg and other CNNs on Camvid test set. This performance achieves the state-of-the-art. To demonstrate compatibility with low compute devices, we evaluated our CNN on two mobile computing platforms and showed real-time performance (57 fps) on Jetson NX 8 GB with TensorRT and 12.65 fps on Jetson Xavier AGX without TensorRT. Our CNN can operate with high accuracy on low compute devices to support system which benefit from semantic information.
Open scholarship in applied linguistics: What, why, and how

In this report, we reviewed Open Scholarship in Applied Linguistics: What, Why, and How, a two-day online symposium held in June 2022. The symposium was the inaugural event of Open Applied Linguistics, a newly established AILA research network dedicated to the promotion of open scholarship in applied linguistics. We started with the background against which Open Applied Linguistics was founded and went on to introduce our understanding of open scholarship. We then summarised and reviewed selected talks and panels from the symposium, ranging from replication and registered reports to the Postprint Pledge. A common thread across our symposium is an ethos of openness, not merely in terms of research practices, but also in the open acknowledgement of and engagement with the many tensions and challenges regarding open scholarship in applied linguistics, from epistemological and cultural to psychological and practical. This, we believe, is the ethos that can truly build us up as a field. While our symposium represents only one of the initial steps in a long journey, we hope to inspire more aligned and sustained efforts across all levels to incrementally and effectively advance our field towards a more open future.
Utilizing Social Networks as Learning Tools to Transform Education

Collaborative learning is made possible through the use of technological communication networks, which provide information sharing places to encourage teamwork. There is a growing trend in colleges across Spain and Latin America, especially in Mexico, to use social networks, one of the most emblematic Web 2.0 tools, to teach students about the history and appeal of the Internet. There was a research done with 414 students from the 14 majors provided at the University of Guadalajara's Los Altos University Center to investigate the efficacy of social networking as a means of education. Seventy-one percent of respondents said they use them for school-related activities; 45 percent use them to study; and 42 percent use them to play, all of which highlights the growing role and influence that social networks are playing in the classroom.
Dual Guided Aggregation Network for Stereo Image Matching

Stereo image dense matching, which plays a key role in 3D reconstruction, remains a challenging task in photogrammetry and computer vision. In addition to block-based matching, recent studies based on artificial neural networks have achieved great progress in stereo matching by using deep convolutional networks. This study proposes a novel network called a dual guided aggregation network (Dual-GANet), which utilizes both left-to-right and right-to-left image matchings in network design and training to reduce the possibility of pixel mismatch. Flipped training with a cost volume consistentization is introduced to realize the learning of invisible-to-visible pixel matching and left−right consistency matching. In addition, suppressed multi-regression is proposed, which suppresses unrelated information before regression and selects multiple peaks from a disparity probability distribution. The proposed dual network with the left−right consistent matching scheme can be applied to most stereo matching models. To estimate the performance, GANet, which is designed based on semi-global matching, was selected as the backbone with extensions and modifications on guided aggregation, disparity regression, and loss function. Experimental results on the SceneFlow and KITTI2015 datasets demonstrate the superiority of the Dual-GANet compared to related models in terms of average end-point-error (EPE) and pixel error rate (ER). The Dual-GANet with an average EPE performance = 0.418 and ER (>1 pixel) = 5.81% for SceneFlow and average EPE = 0.589 and ER (>3 pixels) = 1.76% for KITTI2005 is better than the backbone model with the average EPE performance of = 0.440 and ER (>1 pixel) = 6.56% for SceneFlow and average EPE = 0.790 and ER (>3 pixels) = 2.32% for KITTI2005.
FedMarket: A Cryptocurrency Driven Marketplace for Mobile Federated Learning Services

Federated learning (FL) enables the training of a shared collaborative machine learning model while keeping all the confidential training data on distributed devices.The FL state-of-the-art considers a monopolist FL task publisher.However, we present a FL marketplace where multiple FL task publishers and mobile devices co-exist for a set of diverse and varying learning tasks.Mobile devices participating in the training of FL models provides pay-as-you-go (i.e. using blockchain-based cryptocurrencies) FL training services to the FL task publishers.In the proposed framework, multiple FL task publishers may compete with each other and the participating workers (i.e.mobile devices) can choose one FL task publisher over another for participation in the training of a global model.We utilize code offloading for enabling customized FL pipelines in mobile devices and mitigating the model heterogeneity inherent in varying and changing FL tasks published by the task publishers.Experimental results indicate the efficacy of the proposed framework.
An Ensemble Approach to Computationally Efficient Radiological Anomaly Detection and Isotope Identification

Radiological source search is a challenging task involving detection and identification of weak sources in a constantly changing radiological background. As of now, many radiological source detection algorithms have been proposed; however, their computational complexity and, hence, reliance on power intensive processing units inhibit low-power applications of radiological source search systems. In this work, we introduce the anomaly filter (AF) algorithm; a computationally light, yet effective time-series source detection algorithm based on exponential weighted moving average (EWMA) and Poisson deviance statistics. Then, we demonstrate that the proposed algorithm can be used in ensemble with other more computationally intensive source detection and identification algorithms to achieve both increased detection performance and reduced power consumption. The proposed AF algorithm and the ensemble algorithms were thoroughly benchmarked against several existing source detection and identification algorithms. The results show that the AF algorithm outperforms existing conventional source detection algorithms, and the ensemble approach improves the overall performance of existing source detection and isotope identification algorithms. Furthermore, the AF algorithm and the non-negative matrix factorization approach-based source identification (NMF-ID) algorithm were combined and implemented on a single-board microcontroller, and the power consumption was measured. This ensemble algorithm reduced the power consumption of the NMF-ID algorithm almost by a factor of 100, while improving the detection performance of the overall system.