Reviewerly/exHarmony huggingface dataset:
Distributive Thermometer: A New Unary Encoding for Weightless Neural Networks

The binary encoding of real valued inputs is a crucial part of Weightless Neural Networks.The Linear Thermometer and its variations are the most prominent methods to determine binary encoding for input data but, as they make assumptions about the input distribution, the resulting encoding is sub-optimal and possibly wasteful when the assumption is incorrect.We propose a new thermometer approach that doesn't require such assumptions.Our results show that it achieves similar or better accuracy when compared to a thermometer that correctly assumes the distribution, and accuracy gains up to 26.3% when other thermometer representations assume an unsound distribution.
Deep Semantic Segmentation Models in Computer Vision

Recently, deep learning models have had a huge impact on computer vision applications, in particular in semantic segmentation, in which many challenges are open.As an example, the lack of large annotated datasets implies the need for new semi-supervised and unsupervised techniques.This problem is particularly relevant in the medical field due to privacy issues and high costs of image tagging by medical experts.The aim of this tutorial overview paper is to provide a short overview of the recent results and advances regarding deep learning applications in computer vision particularly for what concerns semantic segmentation.
The role of feature selection in personalized recommender systems

Recommender systems suggest products to users, based on their popularity or the users' preferences.This paper proposes a hybrid personalized recommender system based on users' tastes and also on information available about items.We used a dataset downloaded from Tri-pAdvisor, which contains some information from restaurants (items), such as price range or special diets.Feature selection techniques are employed to analyze the impact that each variable has on personalized recommendations, allowing us to understand not only the process underlying the recommendation to favor the transparency of the system, but also what users value the most when choosing a restaurant.
Investigation into Target Speaking Rate Adaptation for Voice Conversion

Disentangling speaker and content attributes of a speech signal into separate latent representations followed by decoding the content with an exchanged speaker representation is a popular approach for voice conversion, which can be trained with non-parallel and unlabeled speech data.However, previous approaches perform disentanglement only implicitly via some sort of information bottleneck or normalization, where it is usually hard to find a good trade-off between voice conversion and content reconstruction.Further, previous works usually do not consider an adaptation of the speaking rate to the target speaker or they put some major restrictions to the data or use case.Therefore, the contribution of this work is two-fold.First, we employ an explicit and fully unsupervised disentanglement approach, which has previously only been used for representation learning, and show that it allows to obtain both superior voice conversion and content reconstruction.Second, we investigate simple and generic approaches to linearly adapt the length of a speech signal, and hence the speaking rate, to a target speaker and show that the proposed adaptation allows to increase the speaking rate similarity with respect to the target speaker.
Four square method for improving students’ writing ability in EF Teens &amp; Kids Kalimalang

Background: Children are easily to speak up their idea directly but do not know how to shift it into proper writing. The children, English learners, need the writing aspect due to fulfill every English aspect especially in their school academic.&#x0D; Purpose: This research is going to increase the students’ writing ability through eclectic method.&#x0D; Design and methods: This research has another concept with drawing the eclectic method in different ways. The writer tries to put four square writing methods in order to apply the eclectic method in developing students’ writing ability in EF Kids and Teens Kalimalang. This research is a descriptive quantitative research which will be done by field work. In this research, the writer uses the descriptive and quantitative design to analyze the data. The instrument and variable of this research consist of several parts. They are the directive of teaching method, the directive of the test form, and the result test.&#x0D; Results: The combining of Four Square Writing Method and classroom writing performance as Eclectic Method significantly improves the students’ score. It is clearly showed by the increasing of the mean score. The mean score of the pre – test is 68.46% while the mean score of the post – test is 79.13% which increased 10.46%.
Model Pruning Enables Efficient Federated Learning on Edge Devices

Federated learning (FL) allows model training from local data collected by edge/mobile devices while preserving data privacy, which has wide applicability to image and vision applications. A challenge is that client devices in FL usually have much more limited computation and communication resources compared to servers in a datacenter. To overcome this challenge, we propose PruneFL -- a novel FL approach with adaptive and distributed parameter pruning, which adapts the model size during FL to reduce both communication and computation overhead and minimize the overall training time, while maintaining a similar accuracy as the original model. PruneFL includes initial pruning at a selected client and further pruning as part of the FL process. The model size is adapted during this process, which includes maximizing the approximate empirical risk reduction divided by the time of one FL round. Our experiments with various datasets on edge devices (e.g., Raspberry Pi) show that: (i) we significantly reduce the training time compared to conventional FL and various other pruning-based methods; (ii) the pruned model with automatically determined size converges to an accuracy that is very similar to the original model, and it is also a lottery ticket of the original model.
The Research Nexus vision for a more connected scholarly community

Crossref envisions “a rich and reusable open network of relationships connecting research organizations, people, things, and actions; a scholarly record that the global community can build on forever, for the benefit of society”. This Research Nexus expands on the importance of research objects being persistently and uniquely identified. The scholarly community has an established practice of connecting things such as citations to others’ work and it is increasingly critical to identify relationships beyond citations, bringing together published work, unpublished work, institutions, individuals, and identifying the actions that they take e.g., funding, publishing, creating, modifying, citing, and sharing. The Research Nexus brings together metadata and relationships to build a joined-up picture of the scholarly ecosystem and helps everyone identify these relationships and how they change through time. This vision is possible if all parts of the scholarly ecosystem (and beyond) work together, including various scholarly infrastructure organizations.
Combined Pruning for Nested Cross-Validation to Accelerate Automated Hyperparameter Optimization for Embedded Feature Selection in High-Dimensional Data with Very Small Sample Sizes

Abstract Background: Embedded feature selection in high-dimensional data with very small sample sizes requires optimized hyperparameters for the model building process. For this hyperparameter optimization, nested cross-validation must be applied to avoid a biased performance estimation. The resulting repeated training with high-dimensional data leads to very long computation times. Moreover, it is likely to observe a high variance in the individual performance evaluation metrics caused by outliers in tiny validation sets. Therefore, early stopping applying standard pruning algorithms to save time risks discarding promising hyperparameter sets. Result: To speed up feature selection for high-dimensional data with tiny sample size, we adapt the use of a state-of-the-art asynchronous successive halving pruner. In addition, we combine it with two complementary pruning strategies based on domain or prior knowledge. One pruning strategy immediately stops computing trials with semantically meaningless results for the selected hyperparameter combinations. The other is a new extrapolating threshold pruning strategy suitable for nested-cross-validation with a high variance of performance evaluation metrics. In repeated experiments, our combined pruning strategy keeps all promising trials. At the same time, the calculation time is substantially reduced compared to using a state-of-the-art asynchronous successive halving pruner alone. Up to 81.3% fewer models were trained achieving the same optimization result. Conclusion: The proposed combined pruning strategy accelerates data analysis or enables deeper searches for hyperparameters within the same computation time. This leads to significant savings in time, money and energy consumption, opening the door to advanced, time-consuming analyses.
The Implementation of Blended Learning in Teaching Reading

In this rapidly transforming way of teaching, blended learning has become popular across all education levels as it integrates the advantages of traditional classroom activities with online digital media. In the context of teaching English as a foreign language, teachers need to adapt to the use of technology in their teaching activities so that the learning objectives can be achieved successfully. This paper describes the implementation of blended learning in the Engagement Reading class conducted in the English Language Education Department of Universitas Nasional Karangturi. To get the data, the researcher conducted observation, interview, and documentation. The result of the study shows that the stages of building knowledge of the field, modeling of the text, joint construction of the text, and independent construction of the text are applied in the reading class. To facilitate the teaching and learning process, Google Classroom, WhatsApp, Zoom, Mozilla Firefox, and Chrome are utilised. At the end of the course, the students have positive perception toward the implementation of blended learning in the reading class. This is because they feel that the use of technology in blended learning facilitates communication and interaction, engagement, and collaboration. However, they still have problem commonly faced by the students in Indonesia, which is internet connection and credit quota.
Policy Mirror Descent for Reinforcement Learning: Linear Convergence, New Sampling Complexity, and Generalized Problem Classes

We present new policy mirror descent (PMD) methods for solving reinforcement learning (RL) problems with either strongly convex or general convex regularizers. By exploring the structural properties of these overall highly nonconvex problems we show that the PMD methods exhibit fast linear rate of convergence to the global optimality. We develop stochastic counterparts of these methods, and establish an ${\cal O}(1/\epsilon)$ (resp., ${\cal O}(1/\epsilon^2)$) sampling complexity for solving these RL problems with strongly (resp., general) convex regularizers using different sampling schemes, where $\epsilon$ denote the target accuracy. We further show that the complexity for computing the gradients of these regularizers, if necessary, can be bounded by ${\cal O}\{(\log_\gamma \epsilon) [(1-\gamma)L/\mu]^{1/2}\log (1/\epsilon)\}$ (resp., ${\cal O} \{(\log_\gamma \epsilon ) (L/\epsilon)^{1/2}\}$)for problems with strongly (resp., general) convex regularizers. Here $\gamma$ denotes the discounting factor. To the best of our knowledge, these complexity bounds, along with our algorithmic developments, appear to be new in both optimization and RL literature. The introduction of these convex regularizers also greatly expands the flexibility and applicability of RL models.
Uncertainty in three dimensions: the challenges of communicating probabilistic flood forecasts maps

Abstract. Real time operational flood forecasting most often concentrates on issuing streamflow predictions at specific points along the rivers of a watershed. Those points often coincide with gauging stations, and the forecasts can eventually be compared with the corresponding observations for post-event analysis. We are now witnessing an increasing number of studies aimed at also including flood mapping as part of the forecasting system, by feeding the forecasted streamflow to a hydraulics model. While this additional new information (flood extent, depth, velocity, etc.) can potentially be useful for decision makers, it also has the potential to be overwhelming. This is especially true for probabilistic and ensemble forecasting systems. While ensemble streamflow forecasts for a given point in space can be visualized relatively easily, the visualization and communication of probabilistic forecasts for water depth and extent brings additional challenges. The uncertainty becomes three dimensional and it becomes difficult to convey all the important information to support decision-making, while a confusion that could arise from too much information, counter-intuitive interpretation, or simply too much complexity in the representation of the forecast. In this paper, we synthesize the results of a large-scale survey across multiple categories of users of hydrological forecasts (28 government representatives, 52 municipalities, 9 organizations, 37 citizens and farmers, for a total of 139 persons) regarding their preferences in terms of visualizing probabilistic flood forecasts over an entire river reach. Those users have different roles and realities, which influence their needs and preferences. The survey was performed through individual and group interviews during which the interviewees were asked about their needs in terms of hydrological forecasting and their preferences in terms of communication and visualization of the information. In particular, we presented the interviewees with four prototypes representing alternative visualizations of the same probabilistic forecast in order to understand their preferences in terms of colour maps, wording, and the representation of uncertainty. Our results highlight several issues related to the understanding of probabilities in the specific context of visualizing forecasted flood maps. We propose several suggestions for visualizing probabilistic flood maps in order to convey all the relevant information while limiting the confusion of decision makers, and also describe several potential adaptations for different categories of end users.
MOCHA

Users and operators of shared spaces must ensure safety in such areas to prevent the spread of COVID-19. Although each organization has operated a variety of safety-related systems, including contact tracing, congestion monitoring, and check-in services, it is unclear what elements, such as privacy protection level, benefits, and permission procedures, have promoted the usage of these systems. In this study, we created MOCHA, a platform for sharing and tracking room-level locations. This platform automatically detects visited places by scanning Bluetooth beacons in each room using smartphones and shares location data according to predefined user settings. The collected data is used for room-level contact tracing, congestion monitoring, and reservation services. According to >6,500 users' usage data for a year in a university, outlining the advantages of utilizing the app encouraged people to install the app, and reinforced connections in small private groups are encouraged to use the app continuously.
An Asynchronous Scheme for the Distributed Evaluation of Interactive Multimedia Retrieval

Evaluation campaigns for interactive multimedia retrieval, such as the Video Browser Shodown (VBS) or the Lifelog Search Challenge (LSC), so far imposed constraints on both simultaneity and locality of all participants, requiring them to solve the same tasks in the same place, at the same time and under the same conditions. These constraints are in contrast to other evaluation campaigns that do not focus on interactivity, where participants can process the tasks in any place at any time. The recent travel restrictions necessitated the relaxation of the locality constraint of interactive campaigns, enabling participants to take place from an arbitrary location. Born out of necessity, this relaxation turned out to be a boon since it greatly simplified the evaluation process and enabled organisation of ad-hoc evaluations outside of the large campaigns. However, it also introduced an additional complication in cases where participants were spread over several time zones. In this paper, we introduce an evaluation scheme for interactive retrieval evaluation that relaxes both the simultaneity and locality constraints, enabling participation from any place at any time within a predefined time frame. This scheme, as implemented in the Distributed Retrieval Evaluation Server (DRES), enables novel ways of conducting interactive retrieval evaluation and bridged the gap between interactive campaigns and non-interactive ones.
Intelligence-Led Policing Acceptance and Policing Effectiveness: The Roles of Organizational Change, Innovative Behavior and Knowledge Sharing

The study was conducted to examine the linkage between ILP acceptance and policing effectiveness, and to examine the relationships between organizational change, innovative behavior and knowledge sharing practices. Using the quantitative approach, data was collected through a survey from 400 police officers in Abu Dhabi Police (ADP). The data was analyzed for the measurement and structural model, using Smart PLS software. The results of the hypothesis testing suggest the relationship between ILP acceptance and policing effectiveness is significant, and readiness of change is significantly related to ILP acceptance. Furthermore, innovative behavior, knowledge sharing, climate of change and process of change are related to readiness of change. The study concludes ILP enforcement is fundamental for public safety, but the implementation model should consider the factors of organizational change.
Should we agree to disagree about Twitter's bot problem?

Bots, simply defined as accounts controlled by automation, can be used as a weapon for online manipulation and pose a threat to the health of platforms. Researchers have studied online platforms to detect, estimate, and characterize bot accounts. Concerns about the prevalence of bots were raised following Elon Musk's bid to acquire Twitter. Twitter's recent estimate that 5\% of monetizable daily active users being bot accounts raised questions about their methodology. This estimate is based on a specific number of active users and relies on Twitter's criteria for bot accounts. In this work, we want to stress that crucial questions need to be answered in order to make a proper estimation and compare different methodologies. We argue how assumptions on bot-likely behavior, the detection approach, and the population inspected can affect the estimation of the percentage of bots on Twitter. Finally, we emphasize the responsibility of platforms to be vigilant, transparent, and unbiased in dealing with threats that may affect their users.
Ecolinguistics Study on Balinese Lexicons of Memande at Celuk Village Gianyar, Bali, Indonesia

The study on Balinese lexicons related to Memande (silver and gold smith) at Celuk Village in Gianyar Bali concerned with the types of lexicons use in: the materials, tools, products, and activities of the silver and goldsmiths. The study is field research in which the data were gathered in the field by applying in-depth interviews and observation with the silver and gold smiths. The theories of eco-linguistics and sociolinguistics were applied to analyse the data. The findings showed that there were 21 lexicons of tools, 5 lexicons of material, 25 lexicons of products, and 19 lexicons of activities.
A Multi-Strategy Framework for Coastal Waste Detection

In recent years, deep learning has been widely used in the field of coastal waste detection, with excellent results. However, there are difficulties in coastal waste detection such as, for example, detecting small objects and the low performance of the object detection model. To address these issues, we propose the Multi-Strategy Deconvolution Single Shot Multibox Detector (MS-DSSD) based on DSSD. The method combines feature fusion, dense blocks, and focal loss into a state-of-the-art feed-forward network with an end-to-end training style. In the network, we employ feature fusion to import contextual information to boost the accuracy of small object detection. The dense blocks are constructed by a complex function of three concurrent operations, which can yield better feature descriptions. Then, focal loss is applied to address the class imbalance. Due to the lack of coastal waste datasets, data augmentation is designed to increase the amount of data, prevent overfitting of the model, and speed up convergence. Experimental results show that MS-DSSD513 obtains a higher mAP, of 82.2% and 84.1%, compared to the state-of-the-art object detection algorithms on PASCAL VOC2007 and our coastal waste dataset. The proposed new model is shown to be effective for small object detection and can facilitate the automatic detection of coastal waste management.
Positive politeness strategies during online learning: A cyberpragmatic study

Language politeness and character education are crucial in the era of education disruption, particularly in terms of learning communication. Problems with language politeness happen when communication is mediated by electronic media, such as WhatsApp in particular, because it allows undergraduates to be more open in conveying their intentions, including to the lecturers regarding the learning goals. This study aims to describe the forms of language politeness during online learning in higher education. The study used a descriptive qualitative approach. The data of this study were utterances consisting of language politeness, and the data sources were undergraduates’ utterances to their lecturer. The data collection techniques were documenting, reading, and note-taking. The data were analyzed through referential identity, extra lingual interactive model, and politeness strategies proposed by Brown and Levinson (1987) and Maricic (2000). Cyberpragmatic perspective perceives the positive politeness strategies with the help of external context, in this case, the virtual learning context, to find out whether there were positive politeness strategies in the utterances. The findings indicate that positive politeness strategies during online learning using cyberpragmatic approach are represented in six forms: paying attention, using identity markers, looking for agreement, avoiding conflict, creating humor, and showing an optimistic attitude. This study recommends that positive politeness strategies can be used as a pillar of developing and strengthening character education in learning, especially in universities.