Reviewerly/exHarmony huggingface dataset:
Quantifying and Mitigating Cache Side Channel Leakage with Differential Set

Cache side-channel attacks leverage secret-dependent footprints in CPU cache to steal confidential information, such as encryption keys. Due to the lack of a proper abstraction for reasoning about cache side channels, existing static program analysis tools that can quantify or mitigate cache side channels are built on very different kinds of abstractions. As a consequence, it is hard to bridge advances in quantification and mitigation research. Moreover, existing abstractions lead to imprecise results. In this paper, we present a novel abstraction, called differential set, for analyzing cache side channels at compile time. A distinguishing feature of differential sets is that it allows compositional and precise reasoning about cache side channels. Moreover, it is the first abstraction that carries sufficient information for both side channel quantification and mitigation. Based on this new abstraction, we develop a static analysis tool DSA that automatically quantifies and mitigates cache side channel leakage at the same time. Experimental evaluation on a set of commonly used benchmarks shows that DSA can produce more precise leakage bound as well as mitigated code with fewer memory footprints, when compared with state-of-the-art tools that only quantify or mitigate cache side channel leakage.
The Role of Phonological Errors in Evaluation Metrics

In recent years, Natural Language Processing (NLP) has seen a surge in research, particularly in the &#x0D; areas of text summarization and machine translation. Evaluation metrics like ROUGE and BLEU have been &#x0D; widely used to assess the quality of texts using N-gram based approaches. However, these metrics often struggle &#x0D; when applied to data sourced from the internet, such as social media platforms, due to the prevalence of &#x0D; phonological errors. This study focuses on identifying the sources and frequency of phonological errors while &#x0D; addressing the question of whether they should be considered or not. Data from Twitter, a platform known for &#x0D; phonological errors, was collected, and studied, along with existing literature on the subject. The article proposes &#x0D; enhancing existing metrics by integrating edit distance algorithms like Levenshtein or Damerau-Levenshtein. By &#x0D; considering phonological errors in evaluations, this approach aims to improve accuracy and reliability in the NLP &#x0D; and machine translation domains. The ultimate goal of this study is to contribute to more sensitive and reliable &#x0D; evaluation metrics in these fields.
Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments

The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists’ visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney ( n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.
Analyzing Pre-Service English Teachers' Digital Literacy Skills in EFL Teaching

This study analyses the types of digital literacy skills frequently used and how digital literacy skills support pre-service English teachers’ performances. This research used a mixed method with an explanatory sequential design. The data collection method begins with the collection of quantitative data by online questionnaire, then proceed with the collection of qualitative data to help analyze the data obtained quantitatively through observation and interview. The population of this study was pre-service English teachers, the sixth-semester students of the English Education program at Universitas Negeri Makassar who program teaching training including PPL, Kampus Mengajar, and Ajarmi program. One hundred and two pre-service English teachers filled out the questionnaire, while several representatives were chosen for interview. The result showed that (1) various digital literacy skills were used by pre-service English teachers, such as information, technology, media, communication, reproduction, branching, socio-emotional, and real-time thinking skills. However, the use of information literacy has the highest used percentage, which is 54 % in Often category and 0 % in Never category. and (2) pre-service English teachers' use of digital literacy skills supported the teaching performances, including the access to prepare teaching models and media, improving their teaching skills, and producing new creative and innovative teaching content.
Enhancing Image Classification Performance through Discrete Cosine Transformation on Augmented Facial Images using GANs

The continuous advancements in technology are profoundly influencing various domains, including the realm of artificial intelligence. Within this field, the development and training of facial recognition systems have emerged as one of the most prominent research areas. Nowadays, facial recognition systems are rapidly replacing traditional security methods. In order to develop a good face recognition system, the training process must be provided with sufficient data. Recently, the number of open-source data that can help improve the accuracy of face recognition systems is limited. Generative Adversarial Networks (GANs) are a type of machine learning algorithm comprising two interconnected neural networks that engage in a competitive relationship. It is widely used in work domains such as image creation, image manipulation, super-resolution, text visualization, photorealistic images, speech production, and face aging. In the study, the lack of data for training face recognition systems was first solved with synthetic face images obtained with GANs. In the subsequent stage of the investigation, the aim was to enhance the image classification procedure through the application of the discrete cosine transform to the images. This approach aimed to fortify facial recognition systems against the presence of authentic-looking fabricated faces within virtual environments. In the study, it was found that the classification of faces could be improved by 30% compared to the normal classification model. The primary objective of this research endeavor is to make a significant contribution towards the development of highly accurate facial recognition systems.
The knowledge dividend of large language models

Over at the work blog, I’m discussing what knowledge means for large language models (LLMs), and the ways in which we can leverage this knowledge dividend for better inference. As I’m writing this, the sun hasn’t risen over the Denver skyline in earnest. There’s still pink in the sky over the Front Range, and most of the world is still blissfully asleep.
Early Childhood Curriculum Management Decision Making Structure for Sustainable Early Childhood Education

The purpose of this study is to provide a basis for sustainable development education in early childhood education by examining the decision-making structure of early childhood curriculum management as the governance structure of ESG related to sustainable development. The results of examining the five factors in the decision-making structure of early childhood curriculum management, such as children, parents, national level curriculum, directors of early childhood education institutions, and teachers are as follows. Children are divided into maturationism, behaviorism, and constructivism according to the perspective of viewing the development of children, and their influence on the decision-making structure is determined. Based on the stewardship code, parents' demands are reflected in the national level curriculum and directors of early childhood education institutions to affect the decision-making structure. The national level curriculum affects the decision-making structure through a re-understanding of the play-centered curriculum that reflects the educational backgrounds and constructivist educational beliefs of early childhood teachers. Directors of early childhood education institutions affect the decision-making structure according to the type of institution. Finally, teachers affect the decision-making structure according to the process of reforming the national level curriculum and educational beliefs. Therefore, it was discussed that children, parents, national level curriculum, directors of early childhood education institutions, and teachers can affect the decision-making structure of early childhood curriculum according to their perspectives.
Clearing Hurdles: Foreign Aid, Expropriation Risk, and FDI Inflows in Sub-Saharan African Countries

Existing literature has focused on analyzing various determinants of foreign aid disbursement. From the aid-for-policy framework, however, this paper concentrates on the strategic usage of foreign aid by donor countries to influence recipient countries’ adverse policies. Through foreign aid, donor countries expect to earn favorable policy concessions from recipients to promote their economic interests with FDI. Using a dataset containing 28 sub-Saharan African countries for the period 1990-2021, I find that foreign aid mitigates unfavorable investment conditions of the recipients and it further leads to boosting donor countries’ FDI. This result suggests that foreign aid is used strategically, with its main focus being to increase donors’ economic interests in the region.
A Non-Contact Object Delivery System Using Leader-Follower Formation Control for Multi-Robots

Rapid improvements in the area of multi-robot control algorithms pave the way to design and implement robotic swarms to deal with sophisticated tasks including intelligent object transportation systems. It is crucial to manage the structure of the numerous robots to behave like a whole body for task accomplishment. The leader-follower formation control approach offers a simple and reliable way of keeping the swarm formation in appropriate limits to cope with challenging tasks. Autonomous object transportation with multi-robot systems enjoy the benefits of the leader-follower formation control approach. However, most of the developed transportation systems achieve the task by locating the load onto the robots or by pushing the load in the means of a physical contact. These approaches may lead to a hardware or payload damage due to heavy loads or physical contacts respectively. In this study, a novel non-contact object delivery system is introduced for eliminating the drawbacks of physical contact between the robots and the payload. Permanent magnets are used for propulsion of the payload located on a cart with passive casters. The stability of the proposed multi-robot system is satisfied by a formation controller using potential functions method augmented with a cornering action sub-controller. The simulation results verify the effectiveness of the proposed system during a straight motion and cornering with the root mean square values of the distance between the robots as 1.46 × 10-4 [m] and 0.065 [m] respectively.
Workload Management for Sustainability with Storage Systems in Hybrid Cloud Deployment

Sustainable computing has become a critical topic in recent years due to the increasing demand for computing resources and the significant environmental impact of data centers. It aims to minimize the environmental impact of computing systems, while also meeting the growing demand for computing and storage resources. Data storage plays a crucial role in sustainable computing, as it is one of the most energy-intensive components of a data center. Hybrid cloud, which combines on-premises and public cloud resources, can be leveraged to improve the sustainability of storage. In this context, several strategies have been proposed to optimize the energy consumption of storage systems, such as data compression, deduplication, and tiered storage. Additionally, the use of renewable energy sources, such as solar and wind power, can further reduce the carbon footprint. In this paper, we explore the opportunities of sustainable computing in storage in hybrid cloud environments, present a pragmatic approach for data placement advisory for meeting sustainable goals and discuss the potential benefits of adopting the proposed approach to meet sustainable practices in this context.
A Blockchain-Based Hedonic Game Scheme for Reputable Fog Federations

Fog computing empowers the internet of vehicles (IoV) paradigm by offering computational resources near the end users. In this dynamic paradigm, users tend to move in and out of the range of fog nodes which has implications for the quality of service of the vehicular applications. To cope with these limitations, scholars addressed forming federations of fog providers for task offloading purposes. Nonetheless, a few challenges remain a burden for the formation of the federations. The formation mechanisms used to structure the federations of providers are still not fully stable. This causes a problem because a structureless federation can lead to an underperforming infrastructure. Furthermore, most of the literature ignored the honesty metrics of the providers and how trustworthy they are in allocating the agreed-upon resources for processing the tasks. Moreover, adopting a central reputation mechanism is questionable in terms of reliability due to many complications including the lack of consensus. In this work, we develop a Blockchain-based reputation mechanism for assisting the formation of fog federations for IoV applications. Our mechanism comprises on-chain smart contracts for storing and manipulating the providers’ reputations, and an off-chain Hedonic-based formation process that considers the parameters extracted from the chain to build the federations. We develop smart contracts using Solidity and deploy them on the Ethereum Blockchain. We test our mechanism using the EUA dataset as a proof of concept and compare it to other works in the literature. The results obtained show that our approach is able to enhance the overall payoff and quality of service in the IoV paradigm.
Robust Wind-Resistant Hovering Control of Quadrotor UAVs Using Deep Reinforcement Learning

Unmanned Aerial Vehicles (UAVs) have extensive applications such as logistics transportation and aerial photography. However, UAVs are sensitive to winds. Traditional control methods, such as proportional- integral-derivative controllers, generally fail to work well when the strength and direction of winds are changing frequently. In this work deep reinforcement learning algorithms are combined with a domain randomization method to learn robust wind-resistant hovering policies. A novel reward function is designed to guide learning. This reward function uses a constant reward to maintain a continuous flight of a UAV as well as a weight of the horizontal distance error to ensure the stability of the UAV at altitude. A five-dimensional representation of actions instead of the traditional four dimensions is designed to strengthen the coordination of wings of a UAV. We theoretically explain the rationality of our reward function based on the theories of Q-learning and reward shaping. Experiments in the simulation and real-world application both illustrate the effectiveness of our method. To the best of our knowledge, it is the first paper to use reinforcement learning and domain randomization to explore the problem of robust wind-resistant hovering control of quadrotor UAVs, providing a new way for the study of wind-resistant hovering and flying of UAVs.
MPLA-Net: Multiple Pseudo Label Aggregation Network for Weakly Supervised Video Salient Object Detection

Weakly Supervised Video Salient Object Detection (WSVSOD) only requires coarse-grained manual annotations, which can achieve a good trade-off between labeling efficiency and detection performance. In this paper, a Multiple Pseudo Label Aggregation Network (MPLA-Net) is proposed for WSVSOD. Firstly, the video frames that can obtain high-quality pseudo labels are selected to generate multiple pseudo labels, so as to avoid the prejudice of the single label. Moreover, the pseudo label with fine edge information is used to generate the Edge Information Map (EIM). Secondly, MPLA-Net is designed to adequately excavate and utilize the comprehensive saliency cues in multiple pseudo labels to improve the detection accuracy, in which ResNet-50 is adopted as the backbone network. Edge loss, pseudo label loss, self-supervised loss and fusion loss are exploited to jointly supervise and optimize the network training to obtain a robust detection model. Experimental results on five benchmark datasets demonstrate that, compared with existing weakly supervised methods, the proposed method can achieve state-of-the-art detection accuracy with less model parameters and higher detection speed. And the detected salient objects have fine boundaries.
Doctors Assistive System Using Augmented Reality Glass Critical Analysis

Abstract: Surgeons are constantly searching for new technologies that can improve their operating room. They frequently use technologies that enable their industry to provide a better surgery and patient experience early on. Numerous advancements have been recognized as potential disruptive technologies in the surgical workplace as a result of the ongoing improvement of the surgical environment in the digital age. Augmented reality (AR) are rapidly becoming increasingly available, accessible and importantly affordable, hence their application into healthcare to enhance the medical use of data is certain. Whether it relates to anatomy, intraoperative surgery, or post-operative rehabilitation, applications are already being investigated for their role in the surgeons. AR is the addition of artificial information to one or more of the senses that allows the user to perform tasks more efficiently. We propose a system in which important information for the doctors are displayed on semi-transparent glasses included in an AR-headset and therefore are mixed with the real-worldview.
Corpus Linguistics in EFL Language Teaching: Insights From Research and Practice

Corpus linguistics can provide curriculum developers and teachers with theoretical foundations and guidance when deciding learning objectives, identifying materials and methods, and evaluating learner outputs. In this Exposition piece, we discuss how corpus linguistics can inform EFL language teaching in the areas of materials creation, skill development (reading, writing, speaking, and listening), and evaluation, with special attention given to lexico-grammar and vocabulary. We also provide examples from courses delivered at Waseda University to illustrate these approaches. コーパス言語学は、カリキュラム開発者と教師に学習目標の決定、教材と教育方法の特定、学習者の成果の評価を行う際の理論的基礎とガイダンスを提供します。この解説記事では、語彙文法に注意を払いながらコーパス言語学が資料作成、スキル開発 (読み書き、スピーキング、リスニング) および評価の分野におけるEFL言語教育にどのように役立つかを解説します。 また、これらのアプローチを解説するために、早稲田大学で実施されているコースの例も紹介します。
Enhancing Training Data Quality With Visual Analytics

This installment of Computer’s series highlighting the work published in IEEE Computer Society journals comes from IEEE Transactions on Visualization and Computer Graphics.
Acoustic Model for Sichuan Dialect Speech Recognition Based on Deep Neural Network

With the maturity of speech recognition technology, the demand for the popularization of speech recognition in small languages and local dialects is gradually increasing. This article proposes an acoustic model based on deep neural networks for the Sichuan dialect speech recognition task. By training the special pronunciation methods of Sichuan dialect, the model's temporal discrimination ability was improved.
The Application of Cluster Analysis Algorithm in Artificial Intelligence+the Improvement of Learning Performance: From the perspective of students scores analysis

Analysis algorithm plays an important role in artificial intelligence. There are different analysis algorithms. Among them, one of the most classical algorithms is k-means cluster analysis algorithm. On the basis of demand status of students' grades analysis in personal learning system, the research aims to solve the defects because of the unsuitable choose of initial point. At the same while, the data of students' grades analysis has the problems of large quantity and complex data types in personal learning system. The paper links the problems with the innovative application of k-means cluster analysis algorithm. The research uses multiple sampling and one-time clustering to find the optimal improvement algorithm. Through analog system experiment, it proves the application effect and stability of k-means cluster analysis algorithm.
Distribusi Buku pada Penerbit Prokreatif Media selaku Distributor dalam Menjangkau lebih Banyak Pembaca

This study discusses the distribution of books to procreative media publishers as distributors in reaching more readers. This study aims to find out how the distribution of books to Prokreatif Media publishers as distributors reaches more readers. The research method used in this research is a descriptive qualitative approach. Data collection techniques carried out are by interview and observation. The results of this study indicate that the distribution of books to Prokreatif Media publishers is not only done for profit alone, but to broaden the horizons and knowledge of the Indonesian people by collaborating with many parties such as bookstores, libraries, book fairs, online sales, also cooperate with educational institutions. Prokrasi Media Publisher has succeeded in using several book distribution strategies that are effective in reaching more readers and also being able to spread more goodness and benefits through the books that have been published.
Signal Modulation Recognition Algorithm Based on Optimal Neural Network

This paper presents a method of electromagnetic signal modulation recognition based on the combination of optimal neural network and characteristic parameter extraction. The method extracts four characteristic parameters and covers multiple information such as time domain, frequency domain and envelope of signal. At the same time, combined with the superior performance of the optimized neural network in nonlinear approximation, the recognition of modulated signals under low signal-to-noise ratio environment is realized. The simulation results show that this method has good anti-noise performance and recognition accuracy in signal modulation recognition.