Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Reduce Breaches of Aircraft Communication Data

Aircraft are complex systems that rely heavily on monitoring and real-time communications with the base station. During aviation and flight operations, diverse data are gathered from different sources, including the Cockpit Voice Recorder (CVR), Flight Data Recorder (FDR), logbook, passenger data, passenger manifest etc. Given the high sensitivity of flight data, it is an attractive target for adversaries which could result in operational, financial and safety related incidents. Communications between aircraft pilots and air traffic controllers are all unencrypted. The data, mainly audio communication files, are placed openly within data centers on the ground stations which could lead to a serious compromise in security and privacy. One may rely on the cloud owing to its on-demand features but to thwart possible attacks, the data need to be encrypted first, giving rise to the issue of conducting search over encrypted data. This research presents a novel approach for data security in aviation industry by introducing a semantic-based searchable encryption scheme over the cloud. The designed system has proven to be extraordinarily effective for semantic-based searchable encryption at the word and the text level. The rigorous security and complexity analysis shows that the proposed solution provides a high level of security and efficiency and can be effectively deployed in the aviation sector. The designed scheme is tested through a real-world aviation dataset collected to demonstrate the significance of this research. The proof of concept proves to be secure, privacy-preserving and lightweight while resisting distinguishability attacks.
Ethical Dimension of Live Animals as Ready-made Objects in Contemporary Art

Reaction through art, observed among the 20th century artistic movements, can result in artistic works in which animals are made the object of violence. The objections caused by these works sometimes make people question what art is and its value, and sometimes result in legal interventions. It is the forms of expression in contemporary art where idea gets ahead of physical production. In such an atmosphere, 'philosophy of art activities that question what art is' have become almost equivalent to artistic production. However, the controversial works make the concepts of ethics, law or public influence determine what art can or cannot be. In the background of all these developments, advances in the field of biology blurred the boundaries between animals and humans. After such developments, through which the anthropocentric thought system has lost its power, the position of animals in the system will also change. The reappraisal of animals will fundamentally affect the behavior of the human species towards them, undoubtedly. In such a system where the determinants of the concept of ethics are redefined, objectification of animals will not be ethical as before. In this regard, art can no longer be separated from other fields. When the determinant of the boundaries of art is a non-art field, the discussions within the field lose their meaning. This study aims to investigate the limits of contemporary art by examining the reactions to the selected examples of artists whose ways of including animals in their productions are criticized.
The Effectiveness of Using Rubber Ball Throwing Game to Teach Vocabulary

The research aimed to find out student vocabulary mastery before and after being taugh by using Rubber Ball Throwing Game. It used pre eksperiment design. This research was conducted in SMPN 5 Jombang The sample in this study were students of class VIII-F of SMPN 5 Jombang for the academic year 2021-2022. The researcher analyzed the data using t-test through SPSS V.25. After collecting the data, the researcher got the pre-test and post-test scores with the mean pre-test was 79.23. and the mean value of the post test is 89.42. Then, the researcher analyzed the data using the t test, the results of Sig. (2-tailed) which is lower than 0.05. It can be stated that Ha is accepted. Based totally at the results of data analysis, several conclusions were obtained, namely the vocabulary of students before being taught using a rubber ball throwing game which looked low to be better after being taught using a rubber ball throwing game and there was a significant difference between students' vocabulary mastery before and after being taught using a rubber ball throwing game. Thus, it can be concluded that the use of rubber ball throwing game is effective for teaching vocabulary to class VIII-F students of SMPN 5 Jombang.
THE ANALYSIS OF STUDENTS’ DIFFICULTIES IN READING DESCRIPTIVE TEXT AT THE EIGHTH GRADERS OF SMP MUHAMMADIYAH PAGARALAM

ABSTRCT This study aims to analyze the difficulties of students in reading descriptive texts in class VIII SMP Muhammadiyah Pagaralam. This study used a qualitative descriptive method to analyze the data. The population of this study were students of class VIII SMP Muhammadiyah Pagaralam, and the sample was taken by purposive sampling technique. The data of this research is the students’ difficulties in reading descriptive text. Data were collected using interviews and questionnaires. To find out the results of the data that has been given to students , the author uses the percentage formula. The results showed that the difficulties in reading descriptive text was low. It can be seen from the data, they still have difficulties in reading descriptive texts. Regarding the results, the writer suggests that students practice their reading skills specifically in descriptive texts. If they practice more, they will have good reading skills. To the English teacher, the writer suggests giving more explanations and exercises for students related to descriptive texts. Then for other researchers, the writer suggests studying students’ difficulties in reading descriptive texts.
Comparative Evaluation of Diverse Features in Fluency Evaluation of Spontaneous Speech

There have been lots of previous studies on fluency evaluation of spontaneous speech. However, most of them focus on lexical cues, and little emphasis is placed on how diverse acoustic features and deep end-to-end models contribute to improving the performance. In this paper, we describe multi-layer neural network to investigate not only lexical features extracted from transcription, but also consider utterance-level acoustic features from audio data. We also conduct the experiments to investigate the performance of end-to-end approaches with mel-spectrogram in this task. As the speech fluency evaluation task, we evaluate our proposed method in two binary classification tasks of fluent speech detection and disfluent speech detection. Speech data of around 10 seconds duration each with the annotation of the three classes of “fluent,” “neutral,” and “disfluent” is used for evaluation. According to the two way splits of those three classes, the task of fluent speech detection is defined as binary classification of fluent vs. neutral and disfluent, while that of disfluent speech detection is defined as binary classification of fluent and neutral vs. disfluent. We then conduct experiments with the purpose of comparative evaluation of multi-layer neural network with diverse features as well as end-to-end models. For the fluent speech detection, in the comparison of utterance-level disfluency-based, prosodic, and acoustic features with multi-layer neural network, disfluency-based and prosodic features only are better. More specifically, the performance improved a lot when removing all of the acoustic features from the full set of features, while the performance is damaged a lot if fillers related features are removed. Overall, however, the end-to-end Transformer+VGGNet model with mel-spectrogram achieves the best results. For the disfluent speech detection, the multi-layer neural network using disfluency-based, prosodic, and acoustic features without fillers achieves the best results. The end-to-end Transformer+VGGNet architecture also obtains high scores, whereas it is exceeded by the best results with the multi-layer neural network with significant difference. Thus, unlike in the fluent speech detection, disfluency-based and prosodic features other than fillers are still necessary in the disfluent speech detection.
A Nonlinear Filter by Pseudo-Formal Linearization for Nonlinear Time-Varying Systems

This paper is concerned with a nonlinear filter for a general class of nonlinear time-varying systems, by making use of a pseudo-formal linearization method and Kalman filter approach. The given nonlinear time-varying dynamic and measurement equations are piecewisely linearized by the formal linearization method using Taylor expansion so that Kalman filter can be applied for each linearized time-varying equations. Then the resulting filters are smoothly united into a single nonlinear filter by an automatic choosing function. The efficiency of this filter is shown through numerical examples.
Pic2PLex: A Test for Assessing Free Productive Vocabulary

We present Pic2PLex, a new test for assessing free productive vocabulary, the lexicon people use for writing and speaking. Unlike the existing productive vocabulary tests, Pic2PLex uses images as stimuli to let test-takers say something. A test item comprises a set of six different images with a common theme and two answer sections: a tenword section and a description section. Given the image set, test-takers are instructed to write ten words that come to their minds and a brief description of the images in the corresponding sections. We propose an algorithm to generate image sets for the test items from a large image dataset. The algorithm was designed to simultaneously achieve two conflicting goals: making images of a test item share the same theme and making themes among image sets across test items diverse as much as possible. We conducted experiments with Japanese language learners and native Japanese speakers to collect responses using Pic2PLex and existing productive vocabulary tests for comparison. Quantitative and qualitative investigation revealed that (i) Pic2PLex elicits words based on productive lexical knowledge that the test-takers would have acquired, and (ii) the elicited responses can be used in assessments that measure how much productive vocabulary the test-taker has.
Mechanism and Recent Trends of Federated Learning as a Privacy Technology

近年, 異なる組織間でのデータ共有・活用を実現できるプラットホーム (クラウドやブロックチェーンなど) の構築や運用が進んでいる. こうした動きに伴い, 様々な分野において当該プラットホームを利用したデータ共有・活用による社内業務の効率化や新たな価値創出に関する検討が進められている. 特に, 当該業務／サービスにおいて機械学習を利用している組織などでは, こうしたデータ共有・活用により機械学習の性能向上が期待される連合学習 (Federated Learning) と呼ばれる技術が注目されている. 連合学習は, 学習対象のデータセットが複数の組織などで分散管理されている状況において, データセットそのものを当該組織間で共有することなく, 全てのデータセットの特性を反映させた機械学習のモデルを生成できる技術である. 最近では, マーケティングや社内業務の効率などを向上させることを目的とした当該技術の利用に関する検討も活発化している. そこで, 本稿では, 連合学習について概説するとともに, 当該技術に対する攻撃手法とその対策, および実装プラットホームに関する最近の動向について紹介する.
Self-Anomaly-Detection Model Training via Initialized Meta Model

Anomaly detection has become a key challenge affecting the training accuracy of machine learning. Because the training data is usually collected from Internet, many noised samples will be captured and these samples can decrease the model training accuracy. However, because the abnormal samples are difficult to predict when the samples are collected, and the training samples collected may contain many unknown exception categories, and the labels of normal samples may be incorrect, in this case, it is difficult to train an anomaly detection model based on supervised learning to accurately identify the anomaly samples. In this paper, we propose a new unsupervised anomaly detection method based on BiGAN, namely Rt-BiGAN, to identify the outliers in the training data. Firstly, we propose a Bigan network initialization method based on meta-learning algorithm with a small number of normal samples. Then, a self-supervised drop training is designed to improve the detection ability of the model. Finally, we evaluate our Rt-BiGAN over real-world datasets and the simulations results demonstrate that our mechanism is effective to detect the outliers in model training data.
Egocentric Scene Description for the Blind and Visually Impaired

Image captioning methods come short when being used to describe scenes for the blind and visually impaired, because not only do they focus exclusively on salient objects, eliminating background and surrounding information, but they also do not offer egocentric positional descriptions of objects regarding the users, failing by that to give them the opportunity to understand and rebuild the scenes they are in. Furthermore, the majority of solutions neglect depth information, and models are trained solely on 2D (RGB) images, leading to less accurate prepositions and words or phrases' order. In this paper, we will offer the blind and visually impaired more descriptive captions for almost every region present in the image by the use of DenseCap model. Our contribution lies within the use of depth information that will be estimated by AdaBins model in order to enrich captions with positional information regarding the users, helping them understand their surroundings.
Multichannel Cross-Scale Semantic Coherent Attention Network for Image Inpainting

This paper investigates a cross-scale space semantic feature coherent image inpainting approach since it is challenging for the existing image inpainting methods to fuse the semantic feature information effectively. Firstly, we learn the feature semantic relevance step-by-step from the high-level semantic feature map’s attention mechanism and then we apply what we have learned to the preceding low-level feature map. In order to preserve the visual and semantic coherence of image repair, the missing content can be filled by changing attention from deep to shallow in a multiscale manner. A broader receptive field is generated by partial convolution, and semantic feature relevance is achieved using a multiscale cross feature space feature attention mechanism based on semantic attention. This technique improves the extensibility and continuity of the restored images by reconstructing the semantic information of different feature spaces, not only taking into account the reuse of existing semantic space features but also including across feature spaces. The experimental results demonstrated an improvement in PSNR, SSIM, and L1 performance by 10.50%, 0.13%, and 47.09%, respectively, with clear benefits.
Automatic Generation of Personalized Golf Video Using Integrated Technologies and Methods

Golf is one of the most popular sports in the world. The viewership of the professional golfers’ association (PGA) tour broadcasts increased. However, golf broadcasting production is highly professional, expensive and is only for large events. To allow the amateur golfer to own his personalized golf video and share it with his family or friends, this article aims to propose a novel and cost-effective intelligent system to automatically generate golf videos based on numerous integrated technologies and methodologies. Solar power, camera sensor networks, wireless transmission, edge computing, and artificial intelligence technologies were adopted to implement such an intelligent system. To this end, we demonstrate that the proposed system works well. Based on 350 hours of video production, the cost of the 5G solution or the legacy solution is three times the proposed system. The results show that the proposed system can provide a cost-effective solution to generate golf videos and may serve as a reference for other fields that need automatic video generation.
I Shop Therefore I Am: The Artificial Consumer

Can Artificial Intelligence replace humans not only as producers of goods and content, but also as consumers of commodities? AI agents have so far been associated mainly with the production of goods and content—that is, supply—which is only one side of economic processes. The other side is consumption, which drives demand. Is an Artificial Consumer (AC) conceivable? Can artificial consumers support a human–artificial hybrid artificial economy? Could such an economy help increase GDP? Such questions require exploration of the connection between consciousness, consumption, value, economics, and AI. This admittedly speculative paper discusses the potential consequences of introducing artificial consumers. What place would be left in the economic system for humans if AI took over both supply and demand? The final frontier for AI could be our economy: I shop therefore I am.
Implementation of novel polygon‐based obfuscation methods to improve privacy of agricultural data

Abstract Geoprivacy protection is a significant concern when sharing data. To support sustainable land management by leveraging existing agricultural data, research is needed to identify how the polygon nature of static field parcels can be obfuscated to allow data sharing among individuals and organizations. In this study, five adaptive polygon‐based obfuscation methods including PN*Rand, PDonut‐k, PDensity‐k, PAHilb, and PDonut_AHilb methods were developed and applied on the Irish Nutrient Management Planning Online (NMP Online) agricultural dataset. The polygon‐based obfuscation methods introduced in this study were designed with the consideration of properties of spatial polygon objects including the spatial coordinates, shape and size of the polygon, topology, and spatial relationship between adjacent polygons that can be used to identify real‐world objects. These methods were developed to guarantee that there is no false‐identification and non‐unique obfuscation which is important for static polygon objects in terms of accuracy and privacy protection. Qualitative approaches were developed to identify the optimal values of inner and outer radii of donut shape based on k‐anonymity satisfaction and subsequently obtain the optimal value of k‐anonymity. Several evaluation methods were implemented to compare the methods performance. Density‐based methods particularly PDonut‐AHilb provide the best trade‐off between field parcel confidentiality and spatial pattern preservation and should be considered for researchers and practitioners obfuscating polygon data.
Parameter-efficient feature-based transfer for paraphrase identification

Abstract There are many types of approaches for Paraphrase Identification (PI), an NLP task of determining whether a sentence pair has equivalent semantics. Traditional approaches mainly consist of unsupervised learning and feature engineering, which are computationally inexpensive. However, their task performance is moderate nowadays. To seek a method that can preserve the low computational costs of traditional approaches but yield better task performance, we take an investigation into neural network-based transfer learning approaches. We discover that by improving the usage of parameters efficiently for feature-based transfer, our research goal can be accomplished. Regarding the improvement, we propose a pre-trained task-specific architecture. The fixed parameters of the pre-trained architecture can be shared by multiple classifiers with small additional parameters. As a result, the computational cost left involving parameter update is only generated from classifier-tuning: the features output from the architecture combined with lexical overlap features are fed into a single classifier for tuning. Furthermore, the pre-trained task-specific architecture can be applied to natural language inference and semantic textual similarity tasks as well. Such technical novelty leads to slight consumption of computational and memory resources for each task and is also conducive to power-efficient continual learning. The experimental results show that our proposed method is competitive with adapter-BERT (a parameter-efficient fine-tuning approach) over some tasks while consuming only 16% trainable parameters and saving 69-96% time for parameter update.
ProperBERT - Proactive Recognition of Offensive Phrasing for Effective Regulation

This work discusses and contains content that may be offensive or unsettling. Hateful communication has always been part of human interaction, even before the advent of social media. Nowadays, offensive content is spreading faster and wider through digital communication channels. To help improve regulation of hate speech, we introduce ProperBERT, a fine-tuned BERT model for hate speech and offensive language detection specific to English. To ensure the portability of our model, five data sets from literature were combined to train ProperBERT. The pooled dataset contains racist, homophobic, misogynistic and generally offensive statements. Due to the variety of statements, which differ mainly in the target the hate is aimed at and the obviousness of the hate, a sufficiently robust model was trained. ProperBERT shows stability on data sets that have not been used for training, while remaining efficiently usable due to its compact size. By performing portability tests on data sets not used for fine-tuning, it is shown that fine-tuning on large scale and varied data leads to increased model portability.
Preserving Confidentiality against Factorization Attacks using Fake Modulus ($\zeta$) Approach in RSA and its Security Analysis

In today's world, the most vital feature is the security of shared information. To achieve this, various algorithms are offered in cryptosystems, with RSA being one among them. RSA is a robust encryption method, however it can be cracked using a factorization attack. As a result, this work presents an improved RSA algorithm that emphasises RSA's security feature of giving immunity to factorization attacks. The algorithm provides a third variable, which is used as the public key in the network, replacing the common modulus n. The experimental end results, such as differential analysis, performance analysis, and statistical analysis, clearly demonstrated the efficacy of the proposed methodology for secure communication. In addition, the article examines various assaults that could be made against the proposed system and reflects on its effectiveness.
HLS: Hierarchical Lossless Segmentation - A New Approach for Bilevel Image Compression

Imaging plays a significant role in modern applications as medicine, telemetry and archiving. Hence, image compression is among the procedures of preserving the system resources. In this paper, we propose a new lossless compression technique based on hierarchical segmentation, namely hierarchi-callossless segmentation (HLS). The principle of HLS works with bilevel images on hierarchical segmentation until homogeneous segments are obtained. The homogeneous segments are then encoded by an efficient interference-limiting constellation in which each symbol assembles two bits. From the compression performance perspective, findings illustrate that the proposed HLS outperforms the existing work with compression gain up to 6%. From the compression time perspective, HLS introduces moderate computational time among the comparable approaches. Images, that meet the smoothness criterion, highlight the compression and computational efficiencies of our proposal.