Reviewerly/exHarmony huggingface dataset:
Rabi Oscillations, Entanglement and Teleportation in the Anti-Jaynes-Cummings Model

This paper provides a scheme for generating maximally entangled qubit states in the anti-Jaynes-Cummings interaction mechanism, so called entangled anti-polariton qubit states. We demonstrate that in an initial vacuum-field, Rabi oscillations in a cavity mode in the anti-Jaynes-Cummings interaction process, occur in the reverse sense relative to the Jaynes-Cummings interaction process and that time evolution of entanglement in the anti-Jaynes-Cummings interaction process takes the same form as in the Jaynes-Cummings interaction process. With the generated anti-polariton qubit state as one of the initial qubits, we present quantum teleportation of an atomic quantum state by applying entanglement swapping protocol achieving an impressive maximal teleportation fidelity~$F_ρ=1$.
Technical Note: Low cost stage-camera system for continuous water level monitoring in ephemeral streams

Abstract. Monitoring ephemeral and intermittent streams is a major challenge in hydrology. While direct field observations are best to detect spatial patterns of flow persistence, on site inspections are time and labor intensive and may be impractical in difficult-to-access environments. Motivated by latest advancements of digital cameras and computer vision techniques, in this work, we describe the development and application of a stage-camera system to monitor the water level in ungauged headwater streams. The system encompasses a consumer grade wildlife camera with near infrared (NIR) night vision capabilities and a white pole that serves as reference object in the collected images. Time-lapse imagery is processed through a computationally inexpensive algorithm featuring image quantization and binarization, and water level time series are filtered through a simple statistical scheme. The feasibility of the approach is demonstrated through a set of benchmark experiments performed in controlled and natural settings, characterized by an increased level of complexity. Maximum mean absolute errors between stage-camera and reference data are approximately equal to 2 cm in the worst scenario that corresponds to severe hydrometeorological conditions. Our preliminary results are encouraging and support the scalability of the stage-camera in future implementations in a wide range of natural settings.
Computational Analysis versus Human Intuition: A Critical Comparison of Vector Semantics with Manual Semantic Classification in the Context of Plains Cree

A persistent challenge in the creation of semantically classified dictionaries and lexical resources is the lengthy and expensive process of manual semantic classification, a hindrance which can make adequate semantic resources unattainable for under-resourced language communities. We explore here an alternative to manual classification using a vector semantic method, which, although not yet at the level of human sophistication, can provide usable first-pass semantic classifications in a fraction of the time. As a case example, we use a dictionary in Plains Cree (ISO: crk, Algonquian, Western Canada and United States)
How to Address Humans: System Barge-In in Multi-user HRI

This work investigates different barge-in strategies in the context of multi-user Spoken Dialogue Systems. We conduct a pilot user study in which different approaches are compared in view of how they are perceived by the user. For this setting, the Nao robot serves as a demonstrator for the underlying multi-user Dialogue System which interrupts an ongoing conversation between two humans in order to introduce additional information. The results show that the use of supplemental sounds at this task does not necessarily lead to a positive assessment and may influence the user’s perception of the reliability, competence and understandability of the system.
Skills first — An alternative approach to construct knowledge spaces

This paper combines two aspects in the construction of knowledge spaces. The first is to utilize a partial order of skills which belong to a given domain of knowledge to find items or problems which finally constitute a knowledge space or a learning space. Secondly the orders of skills of different (related) areas are coalesced by well known order operations to construct larger spaces. In the first part a so called basic set representation of an order is utilized to considerably simplify the construction of knowledge spaces. In the second part the techniques to “add” and “multiply” orders are applied to given orders resulting in new orders to which the set representation theory of the first part is applied. Properties of the building blocks are identified which can be transferred to the combination and yield properties of the final knowledge space which make their handling a bit more convenient. In particular, places which proved useful in the construction of the first part are identified when these order operations are utilized.
Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction

Value function is the central notion of Reinforcement Learning (RL). Value estimation, especially with function approximation, can be challenging since it involves the stochasticity of environmental dynamics and reward signals that can be sparse and delayed in some cases. A typical model-free RL algorithm usually estimates the values of a policy by Temporal Difference (TD) or Monte Carlo (MC) algorithms directly from rewards, without explicitly taking dynamics into consideration. In this paper, we propose Value Decomposition with Future Prediction (VDFP), providing an explicit two-step understanding of the value estimation process: 1) first foresee the latent future, 2) and then evaluate it. We analytically decompose the value function into a latent future dynamics part and a policy-independent trajectory return part, inducing a way to model latent dynamics and returns separately in value estimation. Further, we derive a practical deep RL algorithm, consisting of a convolutional model to learn compact trajectory representation from past experiences, a conditional variational auto-encoder to predict the latent future dynamics and a convex return model that evaluates trajectory representation. In experiments, we empirically demonstrate the effectiveness of our approach for both off-policy and on-policy RL in several OpenAI Gym continuous control tasks as well as a few challenging variants with delayed reward.
Geophysical prospecting for epithermal gold deposits (a case study from the Maletoivayam gold ore field, Kamchatka Peninsula)

Research subject. The high-sulfidation (HS-type) epithermal gold deposits of the Maletoyvayam ore field, which is located in the volcanic belts of the Kamchatka Peninsula. Materials and methods. The paper presents the results of geophysical and petrophysical studies carried out by “NPP VIRG-Rudgeofizika” (2016–2017 years) in the central part of the Maletoyvayam ore field hosting HS-type gold deposits. Results. The presented case study shows the potential of geophysical prospecting for HS-type epithermal gold deposits located within altered rock areas. On the basis of the conducted magnetic surveys, fragments of a ring structure broken by faults and the hydrothermally altered bodies within the Neogene volcanogenic-sedimentary sequence of the andesite composition were identified. Using the 3D inversion of magnetic data, the shape, size and structure of the hydrothermally altered bodies were estimated. The electrical survey data allowed the hydrothermally altered areas to be differentiated in terms of composition. As a result, zoning of hydrothermally altered areas was conducted, and ore-bearing sites and gold-bearing bodies of monoquartzites in the plan and cross-section were determined. Conclusions. Geophysical techniques for prospecting HS-type epithermal gold deposits are effective in distinguishing important gold-related features within the altered zone thereby facilitating identification of specific drill targets.
Decoding the Digital Gap in Teacher Education: Three Perspectives across the Globe

Educational use of technology is regularly assessed, and results often show a gap between educational policies and what is actually practiced. This chapter will help clarify how teacher educators experience the changing educational contexts due to the digital revolution, how their meaning-making shifts, and how outside forces influence those processes. The results are based on comparative international studies. Central for this study is practitioners’ professional digital competence, their attitudes towards digital technology and the use of digital technology in education. We found that the influence and contribution of digital practice is carried out quite differently across the globe. Our research questions were: How do practitioners experience teaching in a rapidly changing context? How do attitudes change due to top-down governing of education? and What motivates teacher educators to implement digital technology?
Dealing with Deepfakes: Reddit, Online Content Moderation, and Situational Crime Prevention

Purpose: The study makes use of situational crime prevention framework for analyzing online community reactions to the banning of deepfake pornographic content from Reddit.Methodology/approach: Qualitative text analysis of user comments posted to Reddit’s rule-change announcement (N = 582) was carried out. Analysis relied on the original 25 techniques of situational crime prevention that were adapted into a table of activities and mechanisms meant specifically for use with online platforms.Findings: Analysis indicates that Reddit users voiced several shortcomings that are currently present in Reddit’s platform management approach. In particular, users emphasized issues related to the lack of a consistent and transparent approach to community rule enforcement, as users believed the rule changes to be sudden and poorly reasoned. The general reactionary nature of Reddit’s approach to moderating community-harming actions also was a point of emphasis, alongside the platform’s continued rigid stance on freedom of expression, even with regard to illegal and demeaning content. Regarding Reddit and the new rules on involuntary pornography and the sexualization of minors, enforcement of sitewide policy appears contingent on external influences, such as attention from mainstream media or financial matters, rather than stemming from an inherent stance on decreasing community-harming activities.Research limitations: The study only pertains to a specific rule change by Reddit and subsequent reactions from the platform’s community. Future research is needed to test the applicability of the adapted table of 25 techniques of situational crime prevention in the context of other online platforms.Originality/value: First, the study applies the situational crime prevention approach in the context of moderating online platforms. Second, results from the study shed light on current practices in online content moderation from the perspective of criminological theory, as well as inform specific actions that can be taken to decrease the presence of community-harming phenomena and improve the enforcement of sitewide policy rules in general. Finally, by adapting the original 25 techniques of situational crime prevention to online content moderation, the study suggests a tentative roadmap for similar research in the future.
K-Means Algorithm For Clustering Poverty Data in Bangka Belitung Island Province

The Central Bureau of Statistics is a non-ministerial government institution that reports directly to the President. Based on data from The Central Bureau of Statistics in September 2019, the wealth rate in Indonesia was 9.22% and the number of indigent people in Indonesia reached 24.79 million. The poverty rate in the Bangka Belitung Islands Province was low compared to the national level. This is evidenced by 4.62% of people in Bangka Belitung Island Province were indigent people, which is lower than the national average of 9.22%. The data mining techniques using the K-Means Clustering method are used for this study. The research data was taken from the website of the BPS from 2014-2019 which consisted of 7 districts and/or cities with 3 variables. The variables used are the number of indigent people (in thousands), the average length of school education (years), and adjusted per capita expenditure (thousand rupiahs/year). All data is processed by Rapidminer and 3 clusters are carried out, namely: medium cluster level 0, high cluster level 1, and low cluster level 2. Cluster 0 contains districts/cities whose people have the longest average school time, high per capita expenditure, and a large number of indigent people. Cluster 1 contains districts/cities whose people have a short average school time, low per capita expenditure, a moderate number of indigent people. Cluster 2 contains districts/cities whose people have an average school time, moderate per capita expenditure, a small number of indigent people. Based on the result, the government can prioritize Kabupaten Bangka, Kabupaten Bangka Barat, Kabupaten Bangka Selatan in assisting, especially in the cost of education scholarships and social funds as well as other infrastructure improvements for the welfare of the inhabitants of Bangka Belitung Islands Province.
HYDRO-METEOROLOGICAL ASPECTS OF THE 2021 SOUTH KALIMANTAN FLOOD: TOPOGRAPHY, TIDES, AND PRECIPITATION

The 2021 South Kalimantan flood was recorded as the most serious ever to have taken place in the province. It occurred due to high-intensity rain during the period 10-19 January, accompanied by a spring tide. This study provides an overview of the disaster, with reference to the hydro-meteorological conditions (topography, tides, and precipitation). The method used was the analysis of the precipitation and its monthly rainfall pattern anomalies using remote sensing data. A Digital Elevation Model (DEM) was also analyzed to indicate the most noticeably flood-affected area. In certain areas, total precipitation during the ten days reached 672.8 mm, with daily precipitation peaking at 255 mm on January 14, greater than the 25-year return period value. The flood coincided with a spring tide, which peaked at 1.21 m on the evening of January 15. Using 20- year GPM data, it was found that ENSO and IOD coexisted with both the highest and lowest anomalies. With a La Niña event at the end of 2020, a positive precipitation anomaly in 2021 was expected. The extreme precipitation is suspected to be the main driver of the 2021 South Kalimantan flood, whose impact was worsened by the spring tides. This study conducts further research on the correlation between land-use change, rainfall, spring tide and flooding in South Kalimantan. In addition, it is recommended that the government plan flood risk management by prioritizing areas based on vulnerability to climate hazards.
PERILAKU PENCARIAN INFORMASI MELALUI JURNAL ELEKTRONIK OLEH MAHASISWA PRODI ILMU PERPUSTAKAAN MENGGUNAKAN MODEL ELLIS

Information seeking behavior is a micro-level search behavior, which is shown by a person when interacting with all types of information systems. This study examines information seeking behavior through electronic journals using the Ellis model which has 8 (eight) stages of information search, namely starting, chaining, browsing, differentiating, monitoring, extracting, verification, and ending. This study also examines the constraints faced by Library Science students in searching for information through electronic journals. This study uses quantitative methods using purposive sampling technique, with a total of 57 respondents. The results showed that there was a small proportion of Library Science students who did not start, namely collecting initial information through experts, lecturers, or friends. At the chaining stage, some students do not use bibliographies of books, journals, scientific papers and also do not specify keywords before searching. At the browsing stage, a small proportion of students did not limit their search so that the information displayed did not come out of the desired topic. While the obstacles they face in searching for electronic journals are networks that often have problems, difficulty in determining keywords, limited information displayed in electronic journals, limited foreign language skills, and also advertisements that appear in electronic journals.
Applying the Deep Learning Model on an IoT Board for Breast Cancer Detection based on Histopathological Images

Abstract In breast cancer diagnosis, pathologists evaluate microscopic images of tissue samples to determine if it is benign or malignant. The manual examination process could result in delayed diagnosis, which leads to late cancer treatment and can risk lives. In this paper, we proposed an automated, low-cost, and portable breast cancer detection based on histopathological images by using deep learning. The deep learning models were designed by using the Convolutional Neural Network (CNN). This paper compares the performance of the CNN model by using transfer learning utilizing a pre-trained model (VGG16) and the performance of a CNN model without transfer learning. The result shows that transfer learning provides a good base for classification of histopathological images. The model was successfully deployed on a Raspberry Pi, which demonstrates the model efficiency to run on a lightweight and portable processor.
A Socio-Cultural Approach to Evaluating and Designing Reading Comprehension Apps for Language Learning

Mobile devices are increasingly promoted as tools to facilitate ubiquitous and individualized learning, allowing learners to work at their own pace in authentic and meaningful settings. However, in the case of second language learning, there is a paucity of apps and tools related to improving students' reading comprehension in both Spanish and English. Additionally, there are few studies that address the evaluation of applications for reading comprehension and innovation in this field and this is required in order to respond to the needs of transformation in language learning teaching. The authors present an original evaluation of 25 English language learning mobile apps using the iPAC app rubric, which identifies the pedagogical features of mobile learning: personalisation, authenticity, and collaboration. The results indicate that many of the existing apps fail to fully exploit the affordances of mobile learning and collaboration in particular. The findings suggest recommendations for app developers to design comprehension apps that address these shortcomings.
Dialogue Breakdown Detection Using BERT with Traditional Dialogue Features

Despite of the significant improvements of Natural Language Processing with Neural networks such as machine reading comprehensions, chat-oriented dialogue systems sometimes generate inappropriate response utterances that cause dialogue breakdown because of the difficulty of generating utterances. If we can detect such inappropriate utterances and suppress them, dialogue systems can continue the dialogue easily.
Contextualised Cyber Security Awareness Approach for Online Romance Fraud

Action Fraud reported 50 million pounds was lost to romance fraud in 2018, a 27% increase on the previous year, despite an increase in publicity and guidance surrounding the issue. Romance fraud is an ever-increasing issue, and the statistics highlight the need for a proactive, adaptable, and bespoke approach to assist online dating platforms in combatting the problem, providing targeted awareness to customers while improving the user experience of dating platforms. Currently, there is no effective approach for increasing user awareness and providing real-time intervention on romance fraud. Existing methods on the platform focus on identifying, preventing, and stopping threat actors with technological measures rather than educating potential victims. This paper discusses the existing state of romance fraud and proposes a solution to mitigate the problems by developing a targeted awareness approach. The solution can be adopted by online dating platforms for early identification and timely intervention. It includes bespoke advisory messages to be provided to the user and risk categorisation criteria as well as workflows and prototypes to assist platforms with implementation. The results from the primary research clearly support the objectives showing that timely intervention helps to mitigate against fraud, decreasing the likelihood of it occurring. This approach offers demonstrable improvements to dating platforms.
Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing

To address the challenging task of instance-aware human part parsing, a new bottom-up regime is proposed to learn category-level human semantic segmentation as well as multi-person pose estimation in a joint and end-to-end manner. It is a compact, efficient and powerful framework that exploits structural information over different human granularities and eases the difficulty of person partitioning. Specifically, a dense-to-sparse projection field, which allows explicitly associating dense human semantics with sparse keypoints, is learnt and progressively improved over the network feature pyramid for robustness. Then, the difficult pixel grouping problem is cast as an easier, multi-person joint assembling task. By formulating joint association as maximum-weight bipartite matching, a differentiable solution is developed to exploit projected gradient descent and Dykstra's cyclic projection algorithm. This makes our method end-to-end trainable and allows back-propagating the grouping error to directly supervise multi-granularity human representation learning. This is distinguished from current bottom-up human parsers or pose estimators which require sophisticated post-processing or heuristic greedy algorithms. Experiments on three instance-aware human parsing datasets show that our model outperforms other bottom-up alternatives with much more efficient inference.
Deploying Federated Learning in Large-Scale Cellular Networks: Spatial Convergence Analysis

The deployment of federated learning in a wireless network, called federated edge learning (FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model while preserving data privacy. In this work, we study the spatial (i.e., spatially averaged) learning performance of FEEL deployed in a large-scale cellular network with spatially random distributed devices. Both the schemes of digital and analog transmission are considered, providing support of error-free uploading and over-the-air aggregation of local model updates by devices. The derived spatial convergence rate for digital transmission is found to be constrained by a limited number of active devices regardless of device density and converges to the ground-true rate exponentially fast as the number grows. The population of active devices depends on network parameters such as processing gain and signal-to-interference threshold for decoding. On the other hand, the limit does not exist for uncoded analog transmission. In this case, the spatial convergence rate is slowed down due to the direct exposure of signals to the perturbation of inter-cell interference. Nevertheless, the effect diminishes when devices are dense as interference is averaged out by aggressive over-the-air aggregation. In terms of learning latency (in second), analog transmission is preferred to the digital scheme as the former dramatically reduces multi-access latency by enabling simultaneous access.