Miracle-dz/UniFAR-FTUs huggingface dataset:
Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus

Lithium-ion batteries have emerged as the state-of-the-art energy storage for portable electronics, electrified vehicles, and smart grids. An enabling Battery Management System holds the key for efficient and reliable system operation, in which State-of-Charge (SOC) estimation and State-of-Health (SOH) monitoring are of particular importance. In this paper, an SOC and SOH co-estimation scheme is proposed based on the fractional-order calculus. First, a fractional-order equivalent circuit model is established and parameterized using a Hybrid Genetic Algorithm/Particle Swarm Optimization method. This model is capable of predicting the voltage response with a root-mean-squared error less than 10 mV under various driving-cycle-based tests. Comparative studies show that it improves the modeling accuracy appreciably from its second- and third-order counterparts. Then, a dual fractional-order extended Kalman filter is put forward to realize simultaneous SOC and SOH estimation. Extensive experimental results show that the maximum steady-state errors of SOC and SOH estimation can be achieved within 1%, in the presence of initial deviation, noise, and disturbance. The resilience of the co-estimation scheme against battery aging is also verified through experimentation.
Discovering heterogeneous subpopulations for fine-grained analysis of opioid use and opioid use disorders

The opioid epidemic in the United States claims over 40,000 lives per year, and it is estimated that well over two million Americans have an opioid use disorder. Over-prescription and misuse of prescription opioids play an important role in the epidemic. Individuals who are prescribed opioids, and who are diagnosed with opioid use disorder, have diverse underlying health states. Policy interventions targeting prescription opioid use, opioid use disorder, and overdose often fail to account for this variation. To identify latent health states, or phenotypes, pertinent to opioid use and opioid use disorders, we use probabilistic topic modeling with medical diagnosis histories from a statewide population of individuals who were prescribed opioids. We demonstrate that our learned phenotypes are predictive of future opioid use-related outcomes. In addition, we show how the learned phenotypes can provide important context for variability in opioid prescriptions. Understanding the heterogeneity in individual health states and in prescription opioid use can help identify policy interventions to address this public health crisis.
Effects of Blood Vessels on Fat Channel Microwave Communication

This study aims to investigate the reliability of intrabody microwave propagation through the fat tissue in presence of blood vessels. Here, we consider three types of blood vessels with different sizes. We investigate the impact of the number of blood vessels and their alignment on the transmission of microwave signals through the fat channel. In our study, we employ two probes that act as a transmitter and a receiver. The probes are designed to operate at the Industrial, Scientific, and Medical radio band (2.45 GHz). For a channel length of 100 mm, our results indicate that the presence of the blood vessels may increase the channel path loss by $\tilde1.5$ dB and $\tilde4.5$ dB when the vessels are aligned and orthogonally aligned with the fat channel, respectively.
Performance Comparison on Path Establishment and Recovery algorithms in Wireless Ad-Hoc Networks

Mobile Ad-hoc Network (MANET) consists of wireless devices which are used for the initiating and establish communication link between the sender and receiver nodes. Due to the unpredictable mobility of wireless nodes in MANET, node links can be break at any time. Paper has done the survey on different existing routing protocols which initiates the route between the sender and receiver. Different routing protocols have different ways of route initialization process with single or multiple paths at the beginning which can be used for the link recovery when path gets broken. We have studied the path recovery mechanism when link gets failed between any two nodes. Paper also contributes the study and the comparison between the various protocols involved.
Package Delivery System Based on Blockchain Infrastructure

Blockchain technology has been recently applied in many applications including cryptocurrencies like Bitcoin, Internet of Things, healthcare, and supply chain management. The essential properties are the capabilities in doing verification and maintaining data integrity which would be hardly guaranteed in other systems. As millions of packages are daily sent, information related to those packages must be protected since they are prone to attacks such as phishing. In addition, current package delivery systems are mostly implemented on a centralized platform which would be vulnerable to different kind of attacks. In this paper, we propose a package delivery system based on the blockchain technology which would help enhance the security with data integrity and the verification of both users and data. We evaluate the performance of our system using two operations: insertions and reads/writes under high workload. We found that our system performance for both insertions and reads/writes is highly sensitive to the number of chains rather than the number of transactions per chain. But, the time spent for reads/writes is much higher than the time spent for insertions.
Identification of Cardiovascular Diseases Risk Factors among Diabetes Patients Using Ontological Data Mining Techniques

Healthcare environment is rich of data, but still needs knowledge extraction that is necessarily important for saving people lives. Medical Knowledge discovery is a process of extracting knowledge patterns from biomedical data, which is useful and crucial for making effective decisions especially in developing strategies and policies of preventive medical treatments. Data mining methods are the best-known way to recognize the hidden data standards. Ontology engineering used to improve knowledge domain representation, and further is considered for the enhancement and refinement of the mining techniques based on the discovered patterns driven from ontological data mining. In this paper, we apply ontology driven data mining techniques on a data set of diabetes patients who have cardiovascular disease. That process performed to identify the relationship between type two diabetes mellitus patients and their important laboratory tests specified by doctors. Doctors aim to investigate the probability of cardiovascular disease occurrence and stroke happening. Ontology driven Data mining techniques also used in experimental study as well as rule induction, association rules methods. In a late phase, we used frequent pattern discovery and rules induction method using ontological data mining algorithm (RMonto). The findings of this study reveals that the use of ontologies minimizes the number of attributes in the preprocessing stage and helps in all data mining stages; in addition to its important role in ontological data mining, we have a higher learning accuracy ratio exceeding 90%. The results of data mining methods and ontological data mining shows that the significance of some laboratory tests like: LP(a),CRP,HDL,FBG,TG,LDH and Chol to predict CVD risk among T2DM patients with a high accuracy.
WikiSeq: Mining Maximally Informative Simple Sequences from Wikipedia

The problem of ordering documents in a large collection into a sequence that is efﬁcient for learning (both human and machine) is of high practical signiﬁcance, but has not yet been well-formulated. We formulate this problem as mining a maximally informative simple sequence of documents. The mined sequence should be maximally informative in the sense that the reader learns quickly by reading only a few documents, and it should be simple so that the reader is not overwhelmed while trying to learn the content. The task can be posed as: Given that a reader wishes to read (at most) k documents, which documents should be selected from the repository and in what order, so as to provide maximum information. We present the WikiSeq algorithm for this purpose. We also design a metric based on information-gain to help objectively evaluate WikiSeq, and conduct experiments to compare with indicative baselines. Finally, we provide case-studies to subjectively illustrate WikiSeq’s merits.
Psychometric properties of the “Autonomous and Controlled Motivation for Treatment Questionnaire” in women with eating disorders

OBJECTIVE To validate the Autonomous and Controlled Motivation for Treatment Questionnaire (ACMTQ) for use in women with an eating disorder (ED). METHOD Data were available for 463 individuals. We assessed factor structure, internal reliability, test-retest reliability, convergent/divergent validity, and incremental predictive validity. RESULTS Our data showed acceptable fit to our hypothesized model (comparative fit index = 0.92, root mean square error of approximation = 0.09, standardized root mean square residual = 0.09). We found test-retest reliability of 0.73 for both the autonomous (α = 0.85) and controlled (α = 0.80) subscales. Autonomous scores were more strongly associated with motivation measures (β = 0.37; 0.46) than with ED severity measures (β = -0.10; -0.18). Associations between autonomous motivation and symptom improvement over time supported predictive validity. Controlled motivation was associated with lower motivation (β = 0.02; -0.31) and with higher ED severity (β = 0.12; 0.47). CONCLUSION Our results suggest that the ACMTQ is valid for use in women with EDs and lend support to the validity of findings from previous ED studies that have used the ACMTQ.
Minutiae-Based Weighting Aggregation of Deep Convolutional Features for Vein Recognition

Deep convolutional neural network (DCNN) has achieved an outstanding performance in large-scale image recognition task because of its discriminative feature representation ability, and pre-trained DCNN models trained for one task have also been applied to domains that are different from their original purposes. Inspired by this idea, a novel hand-dorsa vein recognition model is constructed by adopting DCNN pre-trained on a large-scale database as a universal feature descriptor. However, due to the sparse distribution property of vein information, it is difficult to employ pre-trained DCNN model to extract discriminative deep convolutional features. Therefore, to obtain useful and discriminative deep convolutional features, a novel minutiae-based weighting aggregation (MWA) method is proposed. In specific, the proposed global max-pooling of preserving spatial position information is applied on the feature maps of convolutional layer to localize the minutiae of vein information, and then the minutiae feature of vein image is regarded as the mask that is named as minutiae feature mask, to select deep convolutional features that contain minutiae feature information of vein image. The final feature representation is formed by concatenating each selected deep convolutional feature that is generated by each minutiae feature mask. Series rigorous experiments on the lab-made database are conducted to evidence the effectiveness and feasibility of the proposed MWA for vein recognition. What’s more, an additional experiment with subset of PolyU database illustrates its generalization ability and robustness.
Early Detection of Distributed Denial of Service Attack in Era of Software-Defined Network

A Distributed Denial of Service (DDoS) attack try to make services or resources unavailable to legitimate customers of that service or resource. These attacks are relatively easy to perform and extremely hard to detect in early stages. Different types of detection techniques were proposed by researcher to detect DDoS in its early stages but due to closed nature of networking devices, no promising results were produced. However, with the advents of Software-Defined Network (SDN), situation has changed. SDN is a cutting edge technology, which separates control plane from data plane. Its features, like programmability and centralized view of network, can be used to overcome the difficulties faced by researches. In this paper, we discussed different detection techniques of DDoS and how these techniques can be made more effective by leveraging the unique features of SDN.
Incorporating Collaborative Ranking Algorithm with Weighted Recursive Autoencoder for Item Recommendation

Collaborative ﬁltering (CF) with implicit feedback is a successful method for recommending items to users, which does not require a knowledge of the items or users. CF methods can be mainly classiﬁed into two categories. One is point-wise regression based and the other is pair-wise ranking based, where the latter one only tries to ﬁnd out the items that users prefer while ignores the items that users dislike, and usually gives out a better recommended item list. The performance of CF-based methods degrades signiﬁcantly when the feedback information is sparse. To address the problem, many kinds of auxiliary information have been utilized such as users’ reviews on items, items’ content and description information, price, brands. In this paper we utilize a weighted recursive autoencoder (RAE) to extract useful features from several heterogeneous auxiliary information and tightly couple the weighted RAE with a pair-wise ranking based CF method. Analysis of the hyperparameters illustrates that auxiliary information from different sources is indeed able to beneﬁt our model. Empirical experiments on six real world datasets show that our method outperforms other state-of-the-art methods.
An Approach to Event Mining Based on Massive Online News

In the network era, online news is intense. How to accurately extract events from the enormous amount of news is the real concerns people have, and it is the focus in academia as well. One of the key technologies to solve this problem is event detecting and tracking technology. This paper proposes an approach based on Single-Pass Clustering Algorithm with the characteristic of news comment to extract events from massive online news. Meanwhile we adopt a custom method in similarity calculation in this paper. According to experimental results, the approach has a good performance in event mining.
[Engineering Paper] Identifying Feature Clones in a Suite of Systems

As part of a module re-unification project of an industrial partner's code, spanning one systems and two derivative systems, the feature-clone variants across these systems have to be extracted, to be later re-unified as singular code elements for re-use. To assist developers with this task, the CoRA (The Code Re-unification Application) tool was designed and implemented. An approach, and the subsequent design of the tool was derived from reflection on manual feature-location/clonedetection efforts on the company's systems, in the first phase of an action research cycle where the approach/implementation will be iteratively trialled, and subsequently refined, in-situ. A pilot study is discussed that leads to the proposed tool. The tool combines a hybrid (textual-static) feature location technique and a textual clone detection technique for featureclone identification. In this paper, the rationale behind the CoRA tool is presented, followed by a tool overview and its implementation details. Finally, an example use case shows how the tool is used to locate clones of a particular feature.
DStore: A Holistic Key-Value Store Exploring Near-Data Processing and On-Demand Scheduling for Compaction Optimization

Log-structured merge tree (LSM-tree)-based key-value stores are widely deployed in large-scale storage systems. The underlying reason is that the traditional relational databases cannot reach the high performance required by big-data applications. As high-throughput alternatives to relational databases, LSM-tree-based key-value stores can support high-throughput write operations and provide high sequential bandwidth in storage systems. However, the compaction process triggers write amplification and is confronted with the degraded write performance, especially under update-intensive workloads. To address this issue, we design a holistic key-value store to explorer near-data processing (NDP) and on-demand scheduling for compaction optimization in an LSM-tree key-value store, named DStore. DStore makes full use of various computing capacities in the host-side and device-side subsystems. DStore dynamically divides the whole host-side compaction tasks into the above two-side subsystems according to two-side different computing capabilities. Meanwhile, the device must be featured with an NDP model. The divided compaction tasks are performed by the host and the device in parallel. In DStore, the NDP-based devices exhibit low-latency and high-bandwidth performance, thus facilitating key-value stores. DStore not only accomplishes compaction for key-value stores but also improves the system performance. We implement our DStore prototype in a real-world platform, and different kinds of testbeds are employed in our experiment. LevelDB and a static compaction optimization using the NDP model (called Co-KV) are used to compare with the DStore in our evaluation. Results show that DStore achieves about $3.7 \times $ performance improvement over LevelDB under the db_bench workload. In addition, DStore-enabled key-value stores outperform LevelDB by a factor of about $3.3 \times $ and 77% in terms of throughput and latency under YCSB benchmark, respectively.
On the Impact of Tokenizer and Parameters on N-Gram Based Code Analysis

Recent research shows that language models, such as n-gram models, are useful at a wide variety of software engineering tasks, e.g., code completion, bug identification, code summarisation, etc. However, such models require the appropriate set of numerous parameters. Moreover, the different ways one can read code essentially yield different models (based on the different sequences of tokens). In this paper, we focus on n-gram models and evaluate how the use of tokenizers, smoothing, unknown threshold and n values impact the predicting ability of these models. Thus, we compare the use of multiple tokenizers and sets of different parameters (smoothing, unknown threshold and n values) with the aim of identifying the most appropriate combinations. Our results show that the Modified Kneser-Ney smoothing technique performs best, while n values are depended on the choice of the tokenizer, with values 4 or 5 offering a good trade-off between entropy and computation time. Interestingly, we find that tokenizers treating the code as simple text are the most robust ones. Finally, we demonstrate that the differences between the tokenizers are of practical importance and have the potential of changing the conclusions of a given experiment.
Optical Flow Based Anomaly Detection in Traffic Scenes

Anomaly is any abnormality in a scene that largely differs from the regular pattern. In Indian traffic scenarios we will find this anomalous behavior of vehicles in a larger scale. In this paper we consider three specific kinds of anomalies that occur on the road, viz. vehicles crossing road, pedestrian crossing roads, and change in motion patterns of vehicles on road. Proposed method can assist the driver as a part of ADAS. Traditional camera based methods detect abnormal motion using orientation only of the optical flow. Our method fuses both magnitude and orientation information. Performance obtained on benchmark ITS dataset is presented and analyzed. An average accuracy of 75.33% was obtained with this method.
Wireless Sensor Networks Based Monitoring of Railway Tracks

Growing demand for railway services necessitates a strong need to develop overall infrastructure, factoring its communication and safety mechanisms. It is vital to have a system to supervise the robustness of tracks and report as needed. Sensor enabled wireless communication promises to provide optimal solutions to reduce rail accidents. Ultrasound enabled wireless sensor network (WSN) technology to provide early warning and prevention of accidents has generated interest in the research community. Two novel prototypes using WSN are designed and tested. The uniqueness of the approach lies in the contact (guide based) and contact-less sensing mechanism. The results obtained are encouraging and show tremendous scope for gainful exploitation in the future.
Prospects for Using Cloud Data Warehouses in Information Systems

In the article, the concept of cloud technologies, the benefits of the cloud approach are considered. There are provided advantages of cloud approach of data processing. Standards for data processing centers that are related to the objects with high requirements for reliability and resiliency of all the segments of the network and engineering infrastructure are reviewed. Levels concepts of data processing centers are analyzed. Also there are offered the essence of the term “cloud technologies” and the urgency of its implementation in today's world, the basic level of deployment of cloud services and its characteristics and basic services provided by cloud systems. This article provides the analysis of software approaches of cloud systems for creating cluster systems and virtualization. The paper describes the advantages and disadvantages of these technologies in detail, examines the physical and programmatic level of cloud technologies and prospects for the use of cloud services at the company level and the national level. There are considered prospects of cloud technologies in information systems.
Detection and Prevention of Copywriting Multimedia Contents in Youtube Using Multi-Keyword Ranking Algorithm

YouTube, an American video sharing website has become the most preferred site for watching videos online, with millions of content creators. YouTube is often said to be the second largest search engine in the world after Google itself, where people spend hours watching videos, generating billions of views. Popularity dynamics of YouTube videos highly depends on the meta-tags, number of view counts and the social dynamics which indicates the interaction between the content creators (channels) and YouTube users. Social networks like videos spreads and sensitivity of YouTube meta-level features provide an important impact on the popularity of videos. Our dataset includes all the videos from the YouTube site and are imported into a centralized database of our system using EmbetiSource: In the context of video analysis, each video is characterized into four attributes: Title, Category, Description, Embedded links. The non-frequent search words used by the users are converted to its equivalent signature using the Digital Signature Algorithm (DSA). We propose a Multi -Keyword Ranking (MKR) algorithm which scales the popular or originally created video using attributes such as view count, number of subscribers, title, description and the comments and ratings generated by users computed using sentimental analysis. The outcome of this proposed work would be an optimized search of the original video that was actually created by the channel.