Reviewerly/exHarmony huggingface dataset:
Research on Medical Image Segmentation Method

Medical image segmentation is one of the key tasks of modern medical computer-aided diagnosis. The purpose is to accurately segment the target area and provide effective help for doctors to diagnose and treat patients. Therefore, the quality of medical image segmentation has a great impact on the doctor's diagnosis of the patient's condition and the subsequent treatment of the patient. Research on medical image segmentation technology at home and abroad, summarized five medical image segmentation methods based on threshold, edge detection, region growth, fuzzy clustering and deep learning, and analyze the performance, summarize the research of modern medical image segmentation specialty.
Prospective Primary Teachers’ Didactic-Mathematical Knowledge in a Service-Learning Project for Inclusion

An analysis of the didactic-mathematical knowledge put into play by a sample of 30 prospective primary teachers during their participation in a Service-Learning program focused on mathematical stimulation as a measure of attention to adolescents at risk of social exclusion is presented. The program aimed to respond to current training demands by promoting the development of mathematics-specific professional competencies, as well as social skills to interact with students, and to positively influence the prospective teachers’ affection towards mathematics and its teaching. It was developed during three academic years in two phases: the learning module was oriented to the presentation of the mathematical stimulus program and the didactical analysis of the STEAM activities knocking it into shape; in the service module, the participants implemented the activities in an educational center with students at risk of social exclusion. The analysis was carried out from the video recordings of the sessions, the future teachers’ written reports with the analysis of the activities, questionnaires on the didactic-mathematical knowledge and a satisfaction test. The results show difficulties on the part of the future teachers to analyze some of the activities of the program, which seems to have its origin in their poor command of the common knowledge of the mathematical content, which also limits them when it comes to managing the activities in the way expected for the stimulus program. All in all, participation in the program was positively assessed by them in relation to its usefulness for their training. The potential benefit of Service-Learning programs such as the one addressed here in the face of a positive impact on the affections towards mathematics is concluded, as a necessary step for a more effective acquisition of didactic-mathematical knowledge and skills in the initial training.
A multi-layer two-dimensional convolutional neural network for sentiment analysis

The sentiment analysis method combining long short-term memory (LSTM) network and one-dimensional convolutional neural network (1D-CNN) has become the focus in sentiment analysis tasks. However, 1D-CNN usually captures local features, and LSTM is time-inefficient due to its serialisation characteristics. To this end, we propose a multi-layer two-dimensional convolutional neural network (ML-2D-CNN) for sentiment analysis. In our method, the character-based integer encoding method is used to retain fine-grained sentiment information. Besides, we innovatively introduce interactive features to improve the dimension of feature vectors and enhance sentiment information. The group expansion strategy is used to form several feature mapping groups, which is conducive to the learning of similar sentiment features in the group. Finally, we exploit multi-layer 2D-CNN to extract and mine the fine-grained information of the text. Extensive experiments are conducted on the SST dataset. The experimental results demonstrate that our proposed method is superior to other baseline and state-of-the-art methods.
Demonstration of WDM-Enabled Ultralow-Energy Photonic Edge Computing

We present experimental demonstrations of ultra-low power edge computing enabled by wavelength division multiplexed optical links and time-integrating optical receivers. Initial experimentation demonstrations show ≲ 10 fJ of optical energy per MAC.
Deciding Whether to Ask Clarifying Questions in Large-Scale Spoken Language Understanding

A large-scale conversational agent can suffer from understanding user utterances with various ambiguities such as ASR ambiguity, intent ambiguity, and hypothesis ambiguity. When ambiguities are detected, the agent should engage in a clarifying dialog to resolve the ambiguities before committing to actions. However, asking clarifying questions for all the ambiguity occurrences could lead to asking too many questions, essentially hampering the user experience. To trigger clarifying questions only when necessary for the user satisfaction, we propose a neural self-attentive model that leverages the hypotheses with ambiguities and contextual signals. We conduct extensive experiments on five common ambiguity types using real data from a large-scale commercial conversational agent and demonstrate significant improvement over a set of baseline approaches.
Brazilian Internet Bill of Rights: The Five Roles of Freedom of Expression

Freedom of expression was given a prominent position in the Brazilian Internet Bill of Rights (Law no. 12.965/2014). It is mentioned five times in the wording of the Law, each time playing a different role. From discussions on how to protect anonymous discourse to the challenges of implementing a regime for Internet intermediaries’ liability, this article reviews how freedom of expression is addressed in the Brazilian Internet Bill of Rights.
SAFRank: Multi-Agent based Approach for Internet Services Selection

In the era of modern world, organization are preferring to adopt smart solutions for their business tasks and managing huge and complex transactions. These solutions are provided through online application infrastructures of Internet of Things (IoT), cloud, fog, and edge computing. In the presence of numerous prospects, the selection benchmark for such offers becomes vibrant, especially, when there is no supportive platform available. Prevailing approaches provide services by evaluating the quality of service parameters, K-Nearest Neighbours (KNN) classifications, k-mean clustering, assigning scores, trustworthiness and fuzzy logic techniques on customer's feedback. However, these approaches classically depend on seeker’ feedback and do ‘not consider interrelationship between the services. Secondly, these techniques do not follow standards derived by well-known organizations like National Institute of Standards and Technology (NIST), International Organization for Standards (ISO), and IEEE. Feedback may be self-generated or biased and leading to inappropriate recommendation to end users. To resolve the issue, we propose multi agent based approach using service association factor that computes interrelationship values among services appearing together in a package as SAFRank and evaluates it on standards along with dynamically defined quality of service parameters. It assists seekers to select the best services on their preferences from pool of IoT and internet services. The technique is tested on leading cloud vendors and results show that it meets the desires of service seekers in all service models in an efficient manner.
Towards DTW-based Unlock Scheme using Handwritten Graphics on Smartphones

Nowadays, due to the increasing capability, mobile devices especially smartphones have become a necessity in people’s daily life, which would store a lot of personal and private information. This makes smartphones a major target for cyber-attackers, i.e., either loss of such mobile deices or illegal access will cause personal data breach and economic damage. Hence it is of great importance to safeguard smartphones from unauthorized access with the purpose of reducing the risk of privacy leakage and economic losses. To achieve this purpose, designing a suitable scheme to unlock phone screen is one promising solution. For instance, Android unlock scheme is a typical example, where users can unlock the phone screen by inputting a correct pattern. However, its password space is low due to the adoption of only nine dots on a 2D grid. In this work, we design a new unlock scheme using handwritten graphics, which uses an improved DTW-based algorithm for authentication. Also, we implement a prototype and evaluate our scheme using both a public dataset (SUSIG) and a self-collected dataset (SCD). Our results indicated that our scheme could achieve 7.12% and 8.75% EER on SUSIG and SCD respectively.
The Volcano Hazards Program — Strategic science plan for 2022–2026

First posted April 27, 2022 For additional information, contact: Volcano Hazards ProgramContact Pubs Warehouse The U.S. Geological Survey (USGS) Volcano Hazards Program (VHP) Strategic Science Plan, developed through discussion with scientists-in-charge of the USGS volcano observatories and the director of the USGS Volcano Science Center, specifies six major strategic goals to be pursued over the next 5 years. The purpose of these goals is to help fulfill the USGS VHP mission to enhance public safety and to minimize social and economic disruption caused by volcanic eruptions in the United States and its territories, through delivery of effective forecasts, warnings, and information on volcano hazards based on scientific understanding of volcanic processes. These six major strategic goals are to (1) continue—and when possible, accelerate—implementation of the National Volcano Early Warning System (NVEWS); (2) improve community preparedness for volcanic hazards by updating and standardizing essential components of volcano hazard assessments and providing training to land managers, emergency responders, and State and local communities; (3) develop the next generation of volcano hazard assessments using geographic information systems and other digital tools; (4) make observations with new instrumentation and take advantage of advances in real-time gas sensors; (5) rebuild the Hawaiian Volcano Observatory and its monitoring capabilities; and (6) form new partnerships and strengthen existing partnerships with other government agencies and with academia and industry, to advance volcano monitoring, increase understanding of volcanic processes, and disseminate USGS information.In its effort to advance volcano science and monitoring techniques, the VHP has identified six scientific targets to pursue over the next 5 years, including: (1) increased understanding of volcano seismicity; (2) improved probabilistic forecasting; (3) deepened grasp of volcano eruption histories and geochronology; (4) newly developed and refined physical models of magmatic systems, leading to better situational awareness and accuracy of eruption forecasts; (5) improved warnings and forecasts of volcanic ash and gas clouds and characterization of volcanic smog sources; and (6) refined lava-flow modeling and forecasting of lava-flow paths.
Duality Temporal-Channel-Frequency Attention Enhanced Speaker Representation Learning

The use of channel-wise attention in CNN based speaker representation networks has achieved remarkable performance in speaker verification (SV). But these approaches do simple averaging on time and frequency feature maps before channel-wise attention learning and ignore the essential mutual interaction among temporal, channel as well as frequency scales. To address this problem, we propose the Duality Temporal-Channel-Frequency (DTCF) attention to re-calibrate the channel-wise features with aggregation of global context on temporal and frequency dimensions. Specifically, the duality attention - time-channel (T-C) attention as well as frequency-channel (F-C) attention - aims to focus on salient regions along the T-C and F-C feature maps that may have more considerable impact on the global context, leading to more discriminative speaker representations. We evaluate the effectiveness of the proposed DTCF attention on the CN-Celeb and VoxCeleb datasets. On the CN-Celeb evaluation set, the EER/minDCF of ResNet34-DTCF are reduced by 0.63%/0.0718 compared with those of ResNet34-SE. On VoxCeleb1-O, VoxCeleb1-E and VoxCeleb1-H evaluation sets, the EER/minDCF of ResNet34-DTCF achieve 0.36%/0.0263, 0.39%/0.0382 and 0.74%/0.0753 reductions compared with those of ResNet34-SE.
Field-of-View-Enlarged Single-Camera 3-D Shape Reconstruction

The 3-D shape reconstruction is a hot topic in computational imaging and many related techniques have been developed. However, most of these techniques have a limited field-of-view (FOV), which results in difficulties on general application. In this article, an FOV-enlarged single-camera 3-D shape reconstruction system is proposed. By placing a saccade mirror in the light path, the proposed system generates a series of virtual cameras and reconstructs the object with multiview images. As the virtual cameras have the same resolution with the real camera, the system gets a larger FOV for reconstruction without sacrificing image resolution. Besides, compared with conventional stereovision systems, the proposed system gives a prior of postures of virtual cameras and simplifies the calibration procedure of extrinsic parameters, which makes it a compact and economical system for application. Reconstruction experiments demonstrate that the proposed system provides an FOV-enlarged and accurate 3-D shape reconstruction. Furthermore, the robustness of the system to environmental conditions is also verified by real-life experiments.
Hyperbolic Personalized Tag Recommendation

Personalized Tag Recommendation (PTR) aims to automatically generate a list of tags for users to annotate web resources, the so-called items, according to users’ tagging preferences. The main challenge of PTR is to learn representations of involved entities (i.e., users, items, and tags) from interaction data without loss of structural properties in original data. To this end, various PTR models have been developed to conduct representation learning by embedding historical tagging information into low-dimensional Euclidean space. Although such methods are effective to some extent, their ability to model hierarchy, which lies in the core of tagging information structures, is restricted by Euclidean space’s polynomial expansion property. Since hyperbolic space has recently shown its competitive capability to learn hierarchical data with lower distortion than Euclidean space, we propose a novel PTR model that operates on hyperbolic space, namely HPTR. HPTR learns the representations of entities by modeling their interactive relationships in hyperbolic space and utilizes hyperbolic distance to measure semantic relevance between entities. Specially, we adopt tangent space optimization to update model parameters. Extensive experiments on real-world datasets have shown the superiority of HPTR over state-of-the-art baselines.
“e-breakout”? Weaponised Interdependence and the Strategic Dimensions of China’s Digital Currency

Abstract The government of China has touted the development of a digital version of its currency as a watershed moment in its ongoing financial modernisation processes. The experimental digital renminbi or eCNY is expected to be fully implemented in the next few years, and thus far its initial benefits have been seen primarily on the domestic level. Yet Beijing is also open to the eventual use of the eCNY for international transactions, which in light of the many governments seeking to develop their own central bank digital currencies raises the question of whether or not this e-currency could develop into a global standard. The concept of “weaponised interdependence”, meaning the ability of states to gain bargaining advantages from global networks through the collection of data and to deny others access and benefits, is an ideal method of gauging the potential of the digital renminbi for enhancing Beijing’s power in global financial markets.
Laboratory Behavior Detection Method Based on Improved Yolov5 Model

With the development of deep learning and big data, behavior detection has become a hot spot in computer vision. Laboratory is an important place for teaching or scientific research. As the subject of the laboratory, laboratory behavior of students determines the quality of experimental teaching. Therefore, this paper took the laboratory as the research scene and proposed a laboratory behavior detection method based on deep learning. Firstly, the common categories of laboratory behaviors were defined and a dataset of laboratory behaviors was established. Then, YOLOv5 model was improved and a laboratory behavior detection method was proposed based on the improved YOLOv5. Lastly, the proposed method was trained and tested based on the laboratory behavior dataset. The experimental results have shown that the improved YOLOv5 model can be well applied to laboratory behavior detection of students. Compared with the original YOLOv5 model, the improved model can better adapt to the data characteristics of the laboratory behavior. Its precision and recall are significantly improved, and mAP (mean average precision) is increased by 2.1%. The proposed laboratory behavior detection method can not only be used to analyze laboratory behavior of students and optimize the experimental teaching. Moreover, it can be extended to remote laboratory surveillance and improve the quality of remote laboratory.
A Comparative Perspective on the Protection of Hate Crime Victims in the European Union

Hate crime victims involved in a criminal procedure experience difficulties that are different from problems encountered by other victims.In trying to meet the specific procedural needs of hate crime victims many EU Member States have introduced protective measures and services in criminal proceedings, but the adopted approaches are widely disparate.By reporting the results of an EU-wide comparative survey into hate crime victims within national criminal procedures the authors aim to: (1) make an inventory of the national (legal) definitions of hate crime and the protection measures available (on paper) for hate crime victims; and (2) critically discuss certain national choices, inter alia by juxtaposing the procedural measures to the procedural needs of hate crime victims to see if there are any lacunae from a victimological perspective.The authors conclude that the Member States should consider expanding their current corpus of protection measures in order to address some of the victims' most urgent needs.
Let’s Authenticate: Automated Certificates for User Authentication

Passwords have numerous drawbacks, and as a result many systems have been designed to replace them.Password replacements have generally failed to dislodge passwords due to the complexity of balancing usability, deployability, and security.However, despite this lack of success, recent advances with password managers and FIDO2 afford new opportunities to explore system design for password replacements.In this work, we explore the feasibility of a system for user authentication based on certificates.Rather than developing new cryptography, we develop a new system, called Let's Authenticate, which combines elements of password managers, FIDO2, and certificates.Our design incorporates feedback from a survey of 397 participants to understand their preferences for system features.Let's Authenticate issues privacy-preserving certificates to users, automatically manages their credentials, and eliminates trust in third parties.We provide a detailed security and privacy analysis, an overhead analysis, and a systematic comparison of the system to a variety of alternatives using a well-known framework.We discuss how Let's Authenticate compares to other systems, lessons learned from our design, and issues related to centralized management of authentication data.
GaVe: A webcam-based gaze vending interface using one-point calibration

Gaze input, i.e., information input via eye of users, represents a promising method for contact- free interaction in human-machine systems. In this paper, we present the GazeVending interface (GaVe), which lets users control actions on a display with their eyes. The interface works on a regular webcam, available on most of today's laptops, and only requires a short one-point calibration before use. GaVe is designed in a hierarchical structure, presenting broad item cluster to users first and subsequently guiding them through another selection round, which allows the presentation of a large number of items. Cluster/item selection in GaVe is based on the dwell time, i.e., the time duration that users look at a given Cluster/ item. A user study (N=22) was conducted to test optimal dwell time thresholds and comfortable human-to-display distances. Users' perception of the system, as well as error rates and task completion time were registered. We found that all participants were able to quickly understand and know how to interact with the interface, and showed good performance, selecting a target item within a group of 12 items in 6.76 seconds on average. We provide design guidelines for GaVe and discuss the potentials of the system.
Aspect-Based Sentiment Analysis of Social Media Data With Pre-Trained Language Models

There is a great scope in utilizing the increasing content expressed by users on social media platforms such as Twitter. This study explores the application of Aspect-based Sentiment Analysis (ABSA) of tweets to retrieve fine-grained sentiment insights. The Plant-based food domain is chosen as an area of focus. To the best of our knowledge this is the first time ABSA task is done for this sector and it is distinct from standard food products because different and controversial aspects arise and opinions are polarized. The choice is relevant because these products can help in meeting the sustainable development goals and improve the welfare of millions of animals. Pre-trained BERT,”Bidirectional Encoder Representations with transformers”, is fine-tuned for this task and stands out because it was trained to learn from all the words in the sentence simultaneously using transformers. The aim was to develop methods to be applied on real life cases, therefore lowering the dependency on labeled data and improving performance were the key objectives. This research contributes to existing approaches of ABSA by proposing data processing techniques to adapt social media data for ABSA. The scope of this project presents a new method for the aspect category detection task (ACD) which does not rely on labeled data by using regular expressions (Regex). For aspect the sentiment classification task (ASC) a semi-supervised learning technique is explored. Additionally Part-of-Speech (POS) tags are incorporated into the predictions. The findings show that Regex is a solution to eliminate the dependency on labeled data for ACD. For ASC fine-tuning BERT on a small subset of data was the most accurate method to lower the dependency on aspect level sentiment data.
Overview of PAN 2022: Authorship Verification, Profiling Irony and Stereotype Spreaders, Style Change Detection, and Trigger Detection

The paper gives a brief overview of the four shared tasks to be organized at the PAN 2022 lab on digital text forensics and stylometry hosted at the CLEF 2022 conference. The tasks include authorship verification across discourse types, multi-author writing style analysis, author profiling, and content profiling. Some of the tasks continue and advance past editions (authorship verification and multi-author analysis) and some are new (profiling irony and stereotypes spreaders and trigger detection). The general goal of the PAN shared tasks is to advance the state of the art in text forensics and stylometry while ensuring objective evaluation on newly developed benchmark datasets.