Reviewerly/exHarmony huggingface dataset:
Developing an Efficient Hybrid Approach for Email Spam Filtering: SPAM-NSGA-II-NvBys

Any type of unwanted email that you didn't sign up to receive is considered spam. However, some spam could be a part of an identity theft scam or another type of fraud. Some spam is annoying but not unpleasant. In order to combat the spam phenomenon's ongoing growth, anti-spam filters have recently become indispensable technologies for Internet service providers. E-mails are filtered inconsistently across different users nevertheless of the user’s curiosity. For statistically figuring out whether a specific email message is actually spam or not, there isn't one particular algorithm. To address this issue, we present a hybrid approach that combines the Navie Bayes spam filtering algorithm with the Multi objective Genetic Algorithm: Non-dominated Sorting Genetic Algorithm (NSGA-II), resulting in a better result in reducing spam mails entering the user's inbox. SPAM-NSGA-II-NvBys will be the name of our proposed hybrid approach. The filter's evaluation revealed that it can make decisions with high accuracy (96.24% in the worst case and 99.66% in the best case).
Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data

Unsupervised Data Augmentation (UDA) is a semi-supervised technique that applies a consistency loss to penalize differences between a model's predictions on (a) observed (unlabeled) examples; and (b) corresponding 'noised' examples produced via data augmentation. While UDA has gained popularity for text classification, open questions linger over which design decisions are necessary and over how to extend the method to sequence labeling tasks. This method has recently gained traction for text classification. In this paper, we re-examine UDA and demonstrate its efficacy on several sequential tasks. Our main contribution is an empirical study of UDA to establish which components of the algorithm confer benefits in NLP. Notably, although prior work has emphasized the use of clever augmentation techniques including back-translation, we find that enforcing consistency between predictions assigned to observed and randomly substituted words often yields comparable (or greater) benefits compared to these complex perturbation models. Furthermore, we find that applying its consistency loss affords meaningful gains without any unlabeled data at all, i.e., in a standard supervised setting. In short: UDA need not be unsupervised, and does not require complex data augmentation to be effective.
Regionally morphing objects for the genetic WASPAS-SVNS game scene generation algorithm

Creative content assistance tools are gaining popularity as they allow one to reduce the time needed to create content in games and other media. Procedural generation allows us to create levels autonomously. The main objective of computational creativity is to replicate results, which are usually created by humans. Our tool is an extension of the genetic WASPAS-SVNS game scene generator. It adds variety to a functional game object type by adding an array of possible visual variations. The morphs of the objects are selected regionally and randomly based on a randomly selected point in the game scene. Results are generated for the 'wall' and 'empty space' object types by adding grass and tree objects to the initial object type. It adds creative value to the level of the game without breaking the rules of game design.
Twist 96-Plex (Riptide) Library Prep v1

This protocol is designed for Twist 96-PLex Library Prep. We use the EPmotion 5075 to add sample barcodes (can also easily be done manually with a multichannel pipette). This is a continuation of the "EPMotion - Normalization and Randomization" protocol.
Web Library Management System Implementation

Asian Journal of Organic & Medicinal Chemistry includes communications, full papers (articles), short papers, green chemistry, reviews, and perspectives. Specifically, these include synthesis, synthetic methods and regents, mechanism of reactions, biologically active compounds with their possible mechanism of action, natural products, in vitro and in vivo tests of compounds, computational chemistry, physical-analytical-inorganic chemistry, nanotechnology, drug carriers, proteomics and genomics. Rapid synthesis of new compounds following eco-friendly methods and their functions as medicinally active agents is certainly the central part of chemical and medicinal research in modern times. On the basis of this fact, significant emphasis is placed to rapid publication and disseminates original discoveries in this new journal.
Literature Study of the Application of Skimming Techniques to Improve Speed ​​Reading Ability in Indonesian Language Lessons for Class V Elementary School Students

The purpose of this study is to describe the process of skimming techniques for elementary school students to improve reading skills. This study uses a literature study approach using the Systematic Literature Review (SLR) method. The subject is a journal that comes from Google Scholar with the research limitations of elementary school students who have been analyzed by researchers. Based on the results of Hawa's research that there is an increase in reading ability in Indonesian language lessons for students. This can be seen from the first and second cycles of active students, educators and student learning outcomes. In the first cycle, the total score for the results of the observation of student activity, the score obtained was 62.5% with a sufficient predicate. While in the second cycle the number of scores obtained on student learning activity got a score of 87.5% with a good and very good predicate. Students. According to Fitria's research, based on the results of the average speed reading ability by skimming on a sample of 16 students, which was adjusted with a five-scale percentage interval to determine the level of student ability. After adjusting for a five-scale score interval, it is known to have obtained a score of 95.25%, therefore this score is included in the Very Good category. It can be proven that students' ability to read quickly using skimming techniques is included in the very good category during the speed reading test and in understanding the content of reading the text. From the results of analyzing previous research conducted by Hawa, Muhtar, and Fitria that skimming technique can help students' speed reading ability in Indonesian language lessons.
The patrix – a new concept for group analysis

This chapter, basis for the presentation ‘Authority and Leadership’ at the 2019 Autumn Workshop of GASI in St. Petersburg, attempts to initiate the development of a group analytic theory of leadership by adding the concept of ‘patrix’ to the term ‘matrix’. The patrix denotes those qualities of the group conductor, which relate to their use of authority and power in the process of forming and instructing the group through interaction with the group. The directive activity of the patrix as a benign and thoughtful authority evokes in the interaction with the structure of the matrix the group analytic processes of ‘ego training in action’. To add the concept of ‘patrix’ to the group, analytic lexicon could be especially helpful to female group conductors.
Massively Parallel Generation of Random Numbers Using a Semiconductor Laser

We employ spatio-temporal interference of many lasing modes for ultrafast, scalable random number generation. The laser diode with a specially-designed cavity produces hundreds of bitstreams at a total rate of two orders-of-magnitude faster than state-of-the-art.
Nonreciprocal light-driven vortex isolator

We report a nonreciprocal reconfigurable light-driven isolator for vortex modes based on topology-selective Brillouin scattering in chiral PCF. The isolation rates are higher than 22 dB and 23 dB for 1 st - and 2 nd -order optical vortices.
Interplay of Quantum and Classical Dynamics in a Generalized Dicke Model

We investigate signatures of classical nonlinear behavior in the vicinity of a superradiant quantum phase transition in a generalized Dicke model. Classical multistable behavior is signalled by spontaneous tunneling between macroscopic quantum states.
Two-color high-purity entanglement

We report continuous variable entanglement between light modes of different colors (852nm and 1064nm). Entanglement is demonstrated from radio-frequency down to the acoustic frequency range. The high-purity state violates the separability limit by -7.5 dB.
Weighted Token-Level Virtual Adversarial Training in Text Classification

Text Classification is the process of classifying text into categories. Among the contextualized architecture of the pretrained model proposed, the Bidirectional Encoder Representations from Transformers (BERT) helps models learn bidirectional contexts of words, making it possible to classify text much more efficiently and accurately. Although BERT and its variance have led to impressive gains on many natural language processing (NLP) tasks, one of the problems of BERT is overfitting. When training data is limited, BERT model overemphasizes certain words and ignores the whole context of the sentence. This makes it difficult for the model to make accurate predictions on the test data. In this paper, we propose weighted token-level virtual adversarial training, which combines two-level perturbations: (1) the sentence-level perturbation and (2) the weighted token perturbation to create a more granular perturbation than traditional virtual adversarial training with only sentence-level perturbation. Our approach can help models learn more about the key and important tokens in sentences when trained with virtual adversarial examples. The experiments in the General Language Understanding Evaluation (GLUE) benchmark showed that our approach can achieve the average score of 79.5%, which outperforms BERT <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">base</inf> model. Our approach can also reduce the overfitting problem especially when datasets are small.
Named Entity Recognition in Military Weapons Domain with Multi-neural Networks Incorporating Relative Position Information

One of the most important problems in the modernization of military information is the study of identifying entities in the sphere of military weaponry, which is critical for military intelligence text processing and military decision-making. The entity identification in the field of military weapons has difficulties and challenges such as lack of public data, low degree of structure and diversity of entity expression. In order to promote the research of named entity recognition in the military weapons field, this paper constructs a military weapons corpus set Chinese weapons based on the network public data, and proposes a model for location coding. The model is a flat lattice structure network incorporating a multi-head self-attention mechanism for feature extraction. It also transforms the entity recognition task into a sequence annotation task and outputs the optimal label sequence. The accuracy, recall, and F value of FLAT-MHA-CRF are 91.47 percent, 91.48 percent, and 91.37 percent, respectively, according to the testing results. The present F value increased the F value of the FLAT-MHA-CRF model compared to the CRF method. Compared to the BiLSTM-CRF model, the FLAT-MHA-CRF model is increased by 0.81%.
A Real-time Visual Target Detection and Automatic Tracking Algorithm Based on YOLOv3 and DWSiamRPN

The current single-target trackers cannot detect the categories of the targets, and multi-object trackers have low performances due to the missed and false detections. Most of these trackers do not have the capabilities for the interesting target selections. To overcome these problems, a real-time visual target detection and automatic tracking algorithm is proposed. First, the video image is detected by using YOLOv3. Then the interesting target is obtained by the proposed intelligent selection strategy. Finally, the selected target is tracked in real-time by the Deeper and Wider Siamese Networks (DWSiamRPN). Experiments show that the proposed YOLOv3-DWSiamRPN method can effectively detect, automatically obtain and track targets in real-time (26 frames per second), with 55.5 mean Average Precision (mAP) on COCO dataset, 0.296 Expect Average Overlaprate (EAO) on VOT-2017 and 0.66 Area Under Curve (AUC) on OTB-2015, respectively. Furthermore, it also addresses the problems of short-term and long-term target losses.
The Bongo-dong Battle(鳳梧洞戰鬪) in 『Utsunomiya Taro Diary』

The Bongo-dong Battle(鳳梧洞戰鬪) in 1920 is one of the most important battles in the War of Independence. Compared to its interest and importance, Bongo-dong Battle still lacks research. In particular, military criminal records and the scale of participation are still controversial. The situation of the Bongo-dong Battle was examined through 『Utsunomiya Taro Diary』 left by Utsunomiya Taro, the commander of the Joseon Army at the time. First, I checked the existence of the Japanese military support unit through 「Hong Beom-do Diary」. The process of Japanese troops' retreat can also be confirmed through 『Utsunomiya Taro Diary』. Second, it is presumed that the Japanese reinforcements were military police(憲兵) or police. Through 『Utsunomiya Taro Diary』, it is understood that the Japanese military's "increase squad" existed in addition to the pursuit team(追擊 隊) of Yasukawa Major(安川少佐). Third, the question of the credibility of 「Bongo-dong Battle Report(鳳梧洞附近戰鬪詳報)」 was dealt with. Through 『Utsunomiya Taro Diary』, it is possible to guess the situation in which the Japanese army was disadvantageous in the Bongo-dong Battle. Regarding the Bongo-dong battle, the Korean data and Japanese data should be cross-verified. In this context, interest in and discovery of Japanese data, including 「Bongo-dong Battle Report」 and 『Utsunomiya Taro Diary』, should continue.
Emerging security challenges in cloud computing: an insight

Cloud computing has been evolved as a new computing prototype with the aim of providing reliability, quality of service and cost-effective with no location barrier. More massive databases and applications are relocated to an immense centralized data center known as the cloud. Cloud computing has enormous benefits of no need to purchase physical space from a separate vendor instead of using the cloud, but these benefits have security threats. The resource virtualization, the data and the machine are physically absent in the cloud; the storage of data in the cloud causes security issues. An unauthorized person can penetrate through the cloud security and can cause data manipulation, data loss or theft might take place. This chapter has described cloud computing and various security issues and challenges that are present in different cloud models and cloud environment. It also gives an idea of different threat management techniques available to encounter security issues and challenges. The RSA algorithm implementation has been described in detail, and the Advance Encryption Standard policy, along with its implementation, has also been discussed. For better clarification, several reviews are conducted on the existing models.
Karonese Sentiment Analysis: A New Dataset and Preliminary Result

Amount social media active users are always increasing and come from various backgrounds. An active user habit in social media is to use their local or national language to express their thoughts, social conditions, socialize, ideas, perspectives, and publish their opinions. Karonese is a non-English language prevalent mostly in North Sumatra, Indonesia, with unique morphology and phonology. Sentiment analysis has been frequently used in the study of local or national languages to obtain an overview of the broader public opinion behind a particular topic. Good quality Karonese resources are needed to provide good Karonese sentiment analysis (KSA). Limitation resources become an obstacle in KSA research. This work provides Karonese Dataset from multi-domain social media. To complete the dataset for sentiment analysis, sentiment label annotated by Karonese transcribers, three kinds of experiments were applied: KSA using machine learning, KSA using machine learning with two variants of feature extraction methods. Machine learning algorithms include Logistic Regression, NaÃ¯ve Bayes, Support Vector Machine and K-Nearest Neighbor. Feature extraction improves model performance in the range of 0.1 â€“ 7.4 percent. Overall, TF-IDF as feature extraction on machine learning has a better contribution than BoW. The combination of the SVM algorithm with TF-IDF is the combination with the highest performance. The value of accuracy is 58.1 percent, precision is 58.5 percent, recall is 57.2, and F1 score is 57.84 percent
An autoethnographic exploration of hybrid identities within education

I offer my story as a way of interrogating innovations around the understanding of ourselves and of the governance of data infrastructures. My doctoral research centred on exploring OneSchool, which is a student information management system a data infrastructure built and used in state schools in Queensland, Australia. When I visited my participating schools, I was introduced as: “This is Jen—she’s OneSchool”. I was not a data infrastructure, and yet those who worked on the School Management Systems project became so connected to each other and associated with the project that in some ways we became synonymous with the system. When introduced as “OneSchool”, I was amused and a little frustrated. I was visiting a school as a doctoral researcher and not as a Queensland Department of Education employee. My story starts six years later, with the hunch that the six-word comment, although unexplored at the time, was important, and remains important. Exploring the diffracted/intra-active becoming of Jen-as-OneSchool allows me to reflect on other identities that I developed and shed along the way. In doing so, I alert those who seek to research within their own spaces of the benefits and risks of being an insider researcher or autoethnographer.
Data Curation from Privacy-Aware Agents

A data curator would like to collect data from privacy-aware agents. The collected data will be used for the benefit of all agents. Can the curator incentivize the agents to share their data truthfully? Can he guarantee that truthful sharing will be the unique equilibrium? Can he provide some stability guarantees on such equilibrium? We study necessary and sufficient conditions for these questions to be answered positively and complement these results with corresponding data collection protocols for the curator. Our results account for a broad interpretation of the notion of privacy awareness. The full version of this paper is available at https://arxiv.org/abs/2207.06929 .
Imagined-Trailing-Whitespace-Agnostic Levenshtein Distance For Plaintext Table Detection

The standard algorithm for Levenshtein distance, treats trailing whitespace the same as any other letter or symbol. However, when humans compare 2 strings, we implicitly assume that both strings are padded by infinite trailing whitespace. This informs our expectations for what the costs for insertion, deletion and replacement, should be. This violation of our expectations results in non-intuitive edit distance values. To account for this specific human intuition, a naive approach which considers "all possible" substrings of trailing whitespace would yield an $O(n^3)$ algorithm. In this work, we provide an efficient $O(n^2)$ algorithm to compute the same. Keywords: Imagined Infinite Trailing Whitespace, Human Friendly, Intuitive Edit Distance, Table Detection, Table Alignment
Model-Based Machine Learning for Communications

We present an introduction to model-based machine learning for communication systems. We begin by reviewing existing strategies for combining model-based algorithms and machine learning from a high level perspective, and compare them to the conventional deep learning approach which utilizes established deep neural network (DNN) architectures trained in an end-to-end manner. Then, we focus on symbol detection, which is one of the fundamental tasks of communication receivers. We show how the different strategies of conventional deep architectures, deep unfolding, and DNN-aided hybrid algorithms, can be applied to this problem. The last two approaches constitute a middle ground between purely model-based and solely DNN-based receivers. By focusing on this specific task, we highlight the advantages and drawbacks of each strategy, and present guidelines to facilitate the design of future model-based deep learning systems for communications.
PENINGKATAN KOMPETENSI DAN MOTIVASI BELAJAR BAHASA INGGRIS MELALUI PENDEKATAN TERPADU (INTEGRATED APPROACH) BAGI MAHASISWA NON BAHASA INGGRIS

English as a subject in the higher education is used as supplement of the core courses. Dealing with the prelimenary observation, the learners do not yet use it maximally. It leads to lack of motivation, vocabulary, learning interest, time allocation, and others. Thus the reseacher chose the Integrated Approach to cope those problems. The study aims at investigating if the Integrated Approach boosts English proficiency and engagement in learning, as well as to see how learners’ progress and what variables affects their progress. it is designed as Classroom Action Research. Accounting major learners of the Islamic University of Indragiri were the respondents. Test results and questionnaires were utilized in gathering the data. Additionally, the research findings reveal that there are better results on the learners’ profeciency and motivation in leearning English. Some integrated processes support their improvement that comprise of civilizing, investigating, observing, controlling, and rehearsing. Moreover, internal and external variables including learner autonomy, instructional approaches, and educator tasks are influencing the advancement. Finally, the use of Integrated leads to better learners' academic achievement and engagement.
Retraction: Research on the Teaching Mode of College English Listening Based on Computer Multimedia Platform (J. Phys.: Conf. Ser. 1648 022013)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 16 September 2022
Retraction: Research on the Cultivation of Cross-cultural Awareness in English Translation Teaching Based on Computer Multimedia Technology (J. Phys.: Conf. Ser. 1648 022036)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 16 September 2022