Reviewerly/exHarmony huggingface dataset:
WattScope: Non-intrusive Application-level Power Disaggregation in Datacenters

Authors:
raw_author_name: Xiaoding Guan
raw_affiliation_string: University of Massachusetts Amherst
raw_author_name: Noman Bashir
raw_affiliation_string: University of Massachusetts Amherst
raw_author_name: David Irwin
raw_affiliation_string: University of Massachusetts Amherst
raw_author_name: Prashant Shenoy
raw_affiliation_string: University of Massachusetts Amherst

Abstract: WattScope is a system for non-intrusively estimating the power consumption of individual applications using external measurements of a server's aggregate power usage and without requiring direct access to the server's operating system or applications. Our key insight is that, based on an analysis of production traces, the power characteristics of datacenter workloads, e.g., low variability, low magnitude, and high periodicity, are highly amenable to disaggregation of a server's total power consumption into application-specific values. WattScope adapts and extends a machine learning-based technique for disaggregating building power and applies it to server- and rack-level power measurements that are already available in datacenters. We evaluate WattScope's accuracy on a production workload and show that it yields high accuracy, e.g., often &lt;∼10% normalized mean absolute error, and is thus a potentially useful tool for datacenters in externally monitoring application-level power usage.
Suboptimal capability of individual machine learning algorithms in modeling small-scale imbalanced clinical data of local hospital

Authors:
raw_author_name: Chenbi Li
raw_affiliation_string: Department of ICU, 3201 Hospital, Hanzhong, Shaanxi, China
raw_author_name: Chengli Wang
raw_affiliation_string: Department of ICU, 3201 Hospital, Hanzhong, Shaanxi, China

Abstract: In recent years, artificial intelligence (AI) has shown promising applications in various scientific domains, including biochemical analysis research. However, the effectiveness of AI in modeling small-scale, imbalanced datasets remains an open question in such fields. This study explores the capabilities of eight basic AI algorithms, including ridge regression, logistic regression, random forest regression, and others, in modeling a small, imbalanced clinical dataset (total n = 387, class 0 = 27, class 1 = 360) related to the records of the biochemical blood tests from the patients with multiple wasp stings (MWS). Through rigorous evaluation using k-fold cross-validation and comprehensive scoring, we found that none of the models could effectively model the data. Even after fine-tuning the hyperparameters of the best-performing models, the results remained below acceptable thresholds. The study highlights the challenges of applying AI to small-scale datasets with imbalanced groups in biochemical or clinical research and emphasizes the need for novel algorithms tailored to small-scale data. The findings also call for further exploration into techniques such as transfer learning and data augmentation, and they underline the importance of understanding the minimum dataset scale required for effective AI modeling in biochemical contexts.
Cryptographic transistor for true random number generator with low power consumption

Authors:
raw_author_name: Hyeong-Jin You
raw_affiliation_string: School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
raw_author_name: Do Hoon Lee
raw_affiliation_string: School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
raw_author_name: Seung-Tak Ryu
raw_affiliation_string: School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
raw_author_name: Yang-Kyu Choi
raw_affiliation_string: School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.

Abstract: We design a cryptographic transistor (cryptoristor)-based true random number generator (tRNG) with low power consumption and small footprint. This is the first attempt to use irregular and unpredictable operation-induced randomness of a cryptoristor as an entropy source. To extract discrete random numbers with a binary code from the cryptoristor, we developed a noise-coupling analog-to-digital converter. This converter not only converts analog signals to digital random bits but also improves the randomness of the entropy source with low power consumption. The randomness of the cryptoristor is attributed to the random carrier multiplications with the creation and stochastic carrier escape as destruction, which occurs iteratively as long as the input current is fed. The cryptoristor-based tRNG passed 15 randomness test suites of NIST Special Publication 800-22. It is robust to iterative operational stresses and to ambient temperature changes, making it an attractive option for hardware-based security solutions in the Internet of Things due to its low power consumption and small size.
Application of machine learning in optical fiber sensors

Authors:
raw_author_name: Yong Zhao
raw_affiliation_string: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China

Abstract: In recent years, with the increasing demand for intelligent society, intelligent photonics has developed rapidly. Machine learning (ML), as a subset of artificial intelligence (AI), has played an important role in the intelligent evolution of optical fiber sensors. Its impact extends beyond enhancing sensor performance by introducing innovative problem-solving approaches. Specifically, ML algorithms have become instrumental in signal demodulation and elevating the efficacy of discrete and distributed sensors, and have also greatly promoted the development of optical fiber speckle pattern processing. This paper presents the latest advancements in ML-based optical fiber sensors, outlines the problems faced by conventional demodulation methods and the common ML algorithms applied in optical fiber sensors, and emphasizes key applications. Additionally, this paper delves into the challenges and future development of this emerging research direction.
Four-state continuous variable quantum key distribution with two accelerating partners

Authors:
raw_author_name: Jiayan Fu
raw_affiliation_string: College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China
raw_author_name: Jian Zhou
raw_affiliation_string: College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China
raw_author_name: Ronghua Shi
raw_affiliation_string: School of Electronic Information, Central South University, Changsha, 410083, PR China
raw_author_name: Jinjing Shi
raw_affiliation_string: School of Electronic Information, Central South University, Changsha, 410083, PR China

Abstract: The field of quantum communication has gradually expanded to include satellites in Earth's orbit. However, for long-distance communication protocols, gravity and its effect on quantum states must be taken into account. By applying the equivalence principle, we can consider that the gravitational effects are equivalent to the acceleration that observers possess. A comprehensive method is proposed to find out the effect of gravity on the performance of the four-state continuous-variable quantum key distribution (CV-QKD) protocol. Finally, the effects of gravity on the QKD of each protocol are discussed, and the associated factors are also analyzed. The performance of the CV-QKD protocol under the effects of gravity is deeply analyzed by taking acceleration into consideration. Both the asymptotic key rate and the finite-size key rate are analyzed in this paper. Furthermore, significant room exists for improving the security and efficiency of space quantum communication.
Intra-hour solar irradiance forecasting using topology data analysis and physics-driven deep learning

Authors:
raw_author_name: Tian Han
raw_affiliation_string: Systems Engineering Institute, School of Automation, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
raw_author_name: Ying Wang
raw_affiliation_string: Systems Engineering Institute, School of Automation, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
raw_author_name: Kang Chen
raw_affiliation_string: Northwest Engineering Corporation Limited, POWERCHINA, Xi'an, Shaanxi, 710065, China
raw_author_name: Huaiwu Peng
raw_affiliation_string: Northwest Engineering Corporation Limited, POWERCHINA, Xi'an, Shaanxi, 710065, China
raw_author_name: Zhenxin Gao
raw_affiliation_string: Northwest Engineering Corporation Limited, POWERCHINA, Xi'an, Shaanxi, 710065, China
raw_author_name: Qinke Peng
raw_affiliation_string: School of Future Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China; Systems Engineering Institute, School of Automation, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China

Abstract: Real-time Direct Normal Irradiance (DNI) prediction is crucial for reliable and economic operation of Concentrated photothermal Solar Power (CSP) system in arid desert areas. However, the stochastic characteristics of short-term multidimensional meteorological time series make intra-hour DNI prediction a challenging task. In this study, we have proposed a deep learning model called TLD, which is combined with topological features captured by Topology Data Analysis (TDA) and temporal features captured by LSTM to address this challenge. Experimental results demonstrated that TLD outperformed the five latest models (Ridge, RF, C_GRU, BiLSTM, and GBRT) on seven solar radiation datasets in arid desert areas. Further analysis revealed that the proportion of cloudy days is a key factor affecting the model's performance. To enhance the forecast ability of TLD, we developed a physics-informed hybrid model named TLDP based on TLD and a smart persistence model, which fully combines the DNI prediction ability of TLD under cloudy conditions and that of the smart persistence model under sunny conditions. Experimental results of eight datasets collected from real-world solar photothermal power stations indicated that TLDP outperformed existing models, which may lay a foundation for more economical and stable operation of CSP plants in arid desert areas.
Diverse semantic information fusion for unsupervised person re-identification

Authors:
raw_author_name: Huafeng Li
raw_affiliation_string: School of Information Engineering and Automation, Kunming University of Science and Technology, Chenggong, Kunming, 650500, Yunnan, China
raw_author_name: Zhanxuan Hu
raw_affiliation_string: School of Information Science and Technology, Yunnan Normal University, Chenggong, Kunming, 650500, Yunnan, China
raw_author_name: Feiping Nie
raw_affiliation_string: School of Computer Science and School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Beilin, Xi’an, 710072, Shaanxi, China

Abstract: Unsupervised Person Re-Identification (Re-ID) has achieved considerable success through leveraging various approaches that rely on hard pseudo-labels. Prior work mainly focused on improving the quality of pseudo-labels or enhancing the robustness of representation learning model. However, there has been little focus on exploring the contextual semantic information, which can reveal rich relations within samples and provide complementary knowledge to assist the hard pseudo-labels. To this end, we propose a novel method named FuseDSI to explore the potential to harness diverse contextual semantic information fusion. In addition to the hard pseudo labels, FuseDSI explores additional pair-wise semantic information and neighborhood semantic information within each mini-batch through online self-exploration. Furthermore, it leverages the explored semantic information as an additional supervisory signal to enhance robust representation learning. For these two types of contextual semantic information are dynamically estimated in an online manner based on the model’s status, they complement each other well with the hard pseudo-labels. One significant advantage of FuseDSI is its flexibility in combining various pseudo-labels-based methods. Moreover, since exploring the contextual semantic information requires no external elaborate module nor memory-consuming memory bank, it maintains the structure of baseline model with negligible impact on training time. Experimental studies on two widely used person ReID benchmark datasets (MSMT17, Market-1501) demonstrate that FuseDSI consistently improves the performance of baseline model and achieves the state-of-the-art results. Code is available at: FuseDSI.
Multimodal representation learning for tourism recommendation with two-tower architecture

Authors:
raw_author_name: Shengbin Liang
raw_affiliation_string: Institute for Data Engineering and Science, University of Saint Joseph, Macau, China; School of Software, Henan University, Kaifeng, China

Abstract: Personalized recommendation plays an important role in many online service fields. In the field of tourism recommendation, tourist attractions contain rich context and content information. These implicit features include not only text, but also images and videos. In order to make better use of these features, researchers usually introduce richer feature information or more efficient feature representation methods, but the unrestricted introduction of a large amount of feature information will undoubtedly reduce the performance of the recommendation system. We propose a novel heterogeneous multimodal representation learning method for tourism recommendation. The proposed model is based on two-tower architecture, in which the item tower handles multimodal latent features: Bidirectional Long Short-Term Memory (Bi-LSTM) is used to extract the text features of items, and an External Attention Transformer (EANet) is used to extract image features of items, and connect these feature vectors with item IDs to enrich the feature representation of items. In order to increase the expressiveness of the model, we introduce a deep fully connected stack layer to fuse multimodal feature vectors and capture the hidden relationship between them. The model is tested on the three different datasets, our model is better than the baseline models in NDCG and precision.
Extraction of time-related expressions using text mining with application to Hebrew

Authors:
raw_author_name: Dror Mughaz
raw_affiliation_string: Dept. of Computer Science, Bar-Ilan University, Ramat-Gan, Israel; Dept. of Computer Science, Jerusalem College of Technology–Lev Academic Center, Jerusalem, Israel
raw_author_name: Yaakov HaCohen-Kerner
raw_affiliation_string: Dept. of Computer Science, Jerusalem College of Technology–Lev Academic Center, Jerusalem, Israel
raw_author_name: Dov Gabbay
raw_affiliation_string: Dep. of Informatics, Kings College London, Strand, London, United Kingdom; Dept. of Computer Science, Bar-Ilan University, Ramat-Gan, Israel

Abstract: In this research, we extract time-related expressions from a rabbinic text in a semi-automatic manner. These expressions usually appear next to rabbinic references (name / nickname / acronym / book-name). The first step toward our goal is to find all the expressions near references in the corpus. However, not all of the phrases around the references are time-related expressions. Therefore, these phrases are initially considered to be potential time-related expressions. To extract the time-related expressions, we formulate two new statistical functions, and we use screening and heuristic methods. We tested these statistical functions, grammatical screenings, and heuristic methods on a corpus containing responsa documents. In this corpus, many rabbinic citations are known and marked. The statistical functions and the screening methods filtered the potential time-related expressions and reduced 99.88% of the initial expressions (from 484,681 to 575).
Privacy-preserving and verifiable data aggregation for Internet of Vehicles

Authors:
raw_author_name: Fucai Zhou
raw_affiliation_string: Software College, Northeastern University, Shenyang 110169, China
raw_author_name: Pengfei Wu
raw_affiliation_string: School of Computing, National University of Singapore, Singapore 117417, Singapore
raw_author_name: Jian Xu
raw_affiliation_string: Software College, Northeastern University, Shenyang 110169, China
raw_author_name: Da Feng
raw_affiliation_string: Software College, Northeastern University, Shenyang 110169, China

Abstract: With the development of Internet of Vehicles (IoV), organizations or institutions are increasingly interested in aggregating vehicle perception data to perform data analysis. Nevertheless, the existing solutions are difficult to meet the comprehensive requirements of IoV scenario, such as low latency, privacy preservation and rich functions. Additionally, it is more challenging that the aggregation node may return tampered or forged aggregation results for vulnerabilities or ulterior motives. To this end, we propose a Privacy-Preserving and Verifiable Data Aggregation Scheme for IoV (PPVDA), which allows the efficient aggregation for inner product over multi-dimensional data, and the data requester can verify the correctness and integrity of the aggregation results. By exploiting homomorphic MAC and secret sharing techniques, we construct a lightweight and verifiable mechanism for results as well as data privacy protection. We also conduct a comprehensive security analysis of PPVDA under the malicious security model. Moreover, extensive theoretical analyses and experimental evaluations demonstrate that PPVDA performs efficiently while retaining more desired properties. When dealing with 4×104 VNs, 3000 RSUs and 500-dimensional vectors, each EN side only takes about 74.5 ms and the DR side only takes about 3.8 ms, is therefore suitable for practical IoV scenarios.
Visual Representation Learning using Graph-based Higher-order Heuristic Distillation for Cell Detection in Blood Smear Images

Authors:
raw_author_name: Jong-Min Lee
raw_affiliation_string: Department of Electronic Engineering, Hanyang University, Seoul 04763, South Korea

Abstract: In many real-world scenarios, including the blood smear domain, it is difficult for detection networks to achieve good performance because image annotation is usually time consuming and expensive. To address this issue, similarity-based distillation (SD) methods, considered the soft version of contrastive learning, are applied to learn a better visual representation without requiring any supervision of the downstream task. Motivated by our theoretical analysis, we treat standard SD methods as the maximization of common 1-hop neighboring key points between two queries in an attributed graph, where nodes represent query and key data points. However, such first-order graph heuristic methods that calculate the likelihood of an unseen link between target nodes by using up to 1-hop neighborhoods are normally limited by insufficient representation power and even lack of generalization ability. Therefore, in this paper, we propose a novel higher-order heuristic distillation (H2D) method that distills knowledge about more general and powerful higher-order heuristic features based on a more than 1-hop relationship in the attributed graph. To do this, we utilize a graph neural network model to learn the higher-order heuristic features on the attributed graph constructed by query and key data representations and transfer the knowledge from the teacher to the student encoder. Our method outperforms the previous state-of-the-art SD methods in the cell detection task on the blood smear dataset as well on open databases (Pascal VOC and MS COCO). Conclusions: Our proposed model allow teacher encoder to transfer the knowledge about more general and powerful higher-order heuristic embeddings to the student and enables better learning for visual representation on cell detection task using blood smear images.
Blockchain technology: A potential tool for the management of pharma supply chain

Authors:
raw_author_name: Pankaj Sharma
raw_affiliation_string: Department of Pharmaceutics, ShriRam College of Pharmacy, Banmore, Morena, Madhya Pradesh, India
raw_author_name: Vinay Jain
raw_affiliation_string: Department of Pharmacognosy, ShriRam College of Pharmacy, Banmore, Morena, Madhya Pradesh, India

Abstract: The pharma supply chain comprises various parties including distributors, manufacturers, raw material suppliers, regulators, pharmacies, hospitals, and patients. Due to the product's complexity and transaction flows, an efficient traceability system is needed in the pharma supply chain to identify the current and all previous product owners. Digitizing the track and trace process significantly improves regulatory oversight and guarantees product quality. A distributed platform for shared data that is immutable, trustworthy, accountable, and transparent in the pharmaceutical supply chain could be built using blockchain-based drug traceability. This review aims to shed light on blockchain technology's significance and necessity for pharmaceutical supply chain management systems. A comprehensive literature review was performed between January 2017 and September 2023. The search was conducted to elaborate on blockchain technology. Blockchain is a software-based technology that logs and records transactions using a block structure arranged chronologically. Cryptography technology links and secures these blocks on a peer-to-peer network. Blockchain is anticipated to transform the pharmaceutical supply chain by giving all participants access to a single, straightforward system that provides transparency, security, and oversight of the end-to-end delivery of goods. In all, various literature data were included in this review. Using a supply chain powered by blockchain has many benefits. To begin with, it gives a thorough account of the entire procedure from start to finish. A single piece of software can manage the entire supply chain. Additionally, it increases communication between parties with permission. The enhanced security and traceability that blockchain offers is another important benefit. A blockchain system can track, trace, and recall products. Blockchain-based pharmaceutical supply chain management enables the tracking of medicinal drug transactions from raw materials suppliers to end consumers. The pharma blockchain has the potential to enhance the security, integrity, data provenance, and functionality of the supply chain due to its transparency, immutability, and auditability.