Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequency tracking techniques for maximum likelihood equalizers for high speed wireless mobile networking in a doubly-faded outdoor channel

This paper presents frequency tracking techniques based on per-survivor channel tracking and phase-locked loops to combat the performance degradation in maximum likelihood sequence estimation due to Doppler shifts and residual carrier offsets. The targeted application is in high bit rate mobile wireless networking in the outdoor environment with mobility of up to 100 km/hr. and achieving a bit rate of 2 Mbps in the 2.4 GHz ISM band. With a low cost frequency reference of 2.5 ppm and a maximum Doppler of 200 Hz, the residual frequency offset felt at the receiver can be as much as 1200 Hz, giving rise to the need for frequency tracking, especially for large data packets. The techniques we propose eliminates the stability and narrow capture range problems exhibited by traditional phase-locked loops when applied to the maximum likelihood equalizer due to the large delay introduced in the feedback loop and shows a factor of 10-100 improvement in the BER at an E/sub b//N/sub 0/ of 20 dB.
Automatic generation of named entity taggers leveraging parallel corpora

The lack of hand curated data is a major impediment to developing statistical semantic processors for many of the world languages. A major issue of semantic processors in Natural Language Processing (NLP) is that they require manually annotated data to perform accurately. Our work aims to address this issue by leveraging existing annotations and semantic processors from multiple source languages by projecting their annotations via statistical word alignments traditionally used in Machine Translation. Taking the Named Entity Recognition (NER) task as a use case of semantic processing, this work presents a method to automatically induce Named Entity taggers using parallel data, without any manual intervention. Our method leverages existing semantic processors and annotations to overcome the lack of annotation data for a given language. The intuition is to transfer or project semantic annotations, from multiple sources to a target language, by statistical word alignment methods applied to parallel texts (Och and Ney, 2000; Liang et al., 2006). The projected annotations can then be used to automatically generate semantic processors for the target language. In this way we would be able to provide NLP processors without training data for the target language. The experiments are focused on 4 languages: German, English, Spanish and Italian, and our empirical evaluation results show that our method obtains competitive results when compared with models trained on gold-standard out-of-domain data. This shows that our projection algorithm is effective to transport NER annotations across languages via parallel data thus providing a fully automatic method to obtain NER taggers for as many as the number of languages aligned via parallel corpora. Automatic Generation of Named Entity Taggers Leveraging Parallel Corpora vii/40
Data Security Scheme for Multiple Attribution Information in Big Data Environment

Recently, as the installation and deletion of not only the Internet but also applications have become easy without any time or place constraints through 3G networks as well as other diverse interfaces such as Wi-Fi and Wibro, the amounts of data are increasing and the kinds of data are being diversified. However, socially large repercussions are arising because methods of protecting user data are insufficient. In the present paper, a security scheme is proposed that will link big data to attribution information to enable users to be provided with big data services from different networks in real time. The proposed scheme inserts attribution information at the beginning and end of contents for provision of big data contents services in real time so that both authentication and non-repudiation can be provided for the contents even if one of the two signatures is lost. In addition, the proposed scheme enhanced the security of contents data by transferring hash chain values to prevent attribution information from being unnecessarily exposed to any 3rd party.
Speeding Up VM Startup by Cooperative VM Image Caching

Virtual machine (VM) management is at the core of virtualized cloud data centers. Among others, how to reduce the startup delay of VMs is a key issue for improving user experience and resource utility. In this paper, we study this issue by jointly considering VM placement and VM image caching. We formulate the joint placement problem and design several joint algorithms, including both online and offline algorithms, to speed up VM startup. In our design, we adopt the cooperative caching approach, where image cache copies are shared among physical machines (PMs) so as to reduce image retrieval time. The key point of our algorithms lies in how to appropriately place VM image cache among PMs so as to speed up VM startup as much as possible. The proposed algorithms are evaluated by extensive simulations via SimGrid. The results show that our algorithms can achieve shorter startup delay in most cases, compared with existing ones.
Clustering Method based on Genetic Algorithm and WordNet

WordNet is a word dictionary interpreting the relationship of the words based on Human Intelligence Science and widely used in such as query term extension of the search engine. However, it is difficult to adapt to the neologism and slang and word meaning change to fast-changing time. Therefore, in this paper we solve the problem of the multiple representations of the words by partly applying the words relationship of the WordNet to Latent Semantic Indexing using by genetic algorithms for more efficient clustering document with the strength and weakness of the Latent Semantic Indexing and WordNet. And with this we try to improve precision and increase the efficiency of the overall clusters. 1
Self-Ownership and Equality: A Lockean Reconciliation

L'A. defend la these selon laquelle le conflit entre la notion libertaire de propriete de la personne et la notion d'egalite peut etre resolu a travers une interpretation egalitariste du principe lockien de justice dans l'acquisition, entendu comme principe de distribution des ressources materielles. Examinant la redistribution de l'imposition, le statut du travail force et les consequences inegalitaires du droit a la propriete chez R. Nozick, ainsi que le probleme du don et du partage entre generations, l'A. montre que l'egalitarisme est compatible avec la conception lockienne des droits a la propriete compris comme condition du bien-etre et non pas du seul point de vue formel
Commentary: Language teacher motivation, autonomy and development in East Asia

COPYRIGHT © 2022 Su and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Commentary: Language teacher motivation, autonomy and development in East Asia
The Esns-Based Traction Electric Quantity Prediction of High-Speed Train with Adaptive Model Inputs Temporal Scale

The precise and reliable speed prediction provides a basis for predicting the traction electric quantity of high-speed train (HST). However, various existing mathematical speed control modes of HST may be insufficiently applicable because of the complex nonlinear and uncertain disturbance. To address this issue, in this paper, a speed predictive model of HST is established by taking the advantages of the efficient and robust prediction capability of echo state networks (ESNs) in dealing with nonlinear time-series. In consideration of the uncertainties of the temporal scale of the model inputs, an adaptive temporal scale selection criterion is set up to further enhance the model prediction accuracy. Then, based on the given traction characteristic curve of HST, the ESNs-based traction electric quantity prediction framework is established. Some experimental results illustrate that the proposed predictive framework can achieve a good performance in terms of predicting accuracy and reliability.
Chinese SLE Treatment and Research Group Registry (CSTAR) XIV: the subjective well-being of patients with systemic lupus erythematosus

Background Systemic lupus erythematosus (SLE) can significantly influence patients’ quality of life and subjective well-being (SWB), but the relationships between clinical characteristics, SWB, and related psychological factors have been little studied. Objective To measure SWB in patients with SLE and examine how major clinical determinants, emotional variables, and related positive factors affect SWB. Methods Overall, 1,110 patients with SLE from the Chinese SLE Treatment and Research Group (CSTAR) and 198 age and gender-matched individuals from the general population without self-reported SLE were invited to complete questionnaires of SWB evaluated by the satisfaction with life scale (SWLS), emotional variables assessed by the patient health questionnaire-9 (PHQ-9), and general anxiety disorder-7 (GAD-7) and related positive factors assessed by the self-esteem scale (SES), general self-efficacy scale (GESE), and Connor-Davidson resilience scale (CD-RISC). The multivariate linear regression was used to examine the relationship between clinical manifestations and SWB. Results Life satisfaction was significantly lower (p < 0.001) in patients with SLE than in the general population. Active skin involvement (OR = 0.923, 95% CI = 0.868–0.981, p < 0.05) was negatively associated with life satisfaction scores, and age at enrollment (OR = 1.160, 95% CI = 1.092–1.230, p < 0.001) were positively associated with life satisfaction scores in the multivariate regression model. The cumulative organ damage was significantly associated with depression (OR = 1.085, 95% CI = 1.022–1.153, p < 0.01) and the loss of self-esteem (OR = 1.067, 95% CI = 1.004–1.133, p < 0.05). Conclusion SWB provides useful insight into the impact of SLE on psychological health and opportunities to improve quality of life and clinical care.
ANALISIS SEGMENTASI PELANGGAN MENGGUNAKAN KOMBINASI RFM MODEL DAN TEKNIK CLUSTERING

Intisari – Persaingan yang ketat di bidang bisnis memotivasi sebuah usaha kecil dan menengah (UKM) untuk mengelola pelayanan terhadap konsumen tetap (pelanggan) secara maksimal. Meningkatkan kesetiaan pelanggan dengan mengelompokkan pelanggan menjadi beberapa kelompok dan menentukan strategi pemasaran yang tepat dan efektif untuk setiap kelompok. Segmentasi pelanggan dapat dilakukan melalui pendekatan data mining dengan metode clustering. Tujuan utamanya segmentasi pelanggan dan mengukur kesetiaan mereka terhadap sebuah produk UKM. Dengan menggunakan metode CRISP-DM yang terdiri dari enam fase, yakni pemahaman bisnis (business understanding), pemahaman data (data understanding), persiapan data (data preparation), pemodelan (modelling), evaluasi (evaluation), dan penerapan (deployment). Algoritma K-means digunakan untuk pembentukan klaster dan RapidMiner sebagai tool yang digunakan untuk mengevaluasi klaster-klaster yang terbentuk. Pembentukan klaster didasarkan pada analisa RFM ( Recency, Frequency, dan Monetary). Davies Bouldin Index (DBI) digunakan untuk menemukan jumlah cluster (k) yang optimal. Hasilnya kelompok pelanggan yang terbentuk ada tiga kelompok dengan kelompok pertama berjumlah 30 pelanggan masuk dalam kategori typical customer, kelompok kedua terdapat 8 pelanggan yang masuk dalam kategori superstar dan kelompok ketiga berjumlah 89 pelanggan pada kategori dormant customer.
Collaborative Filtering as a Multi-Armed Bandit

Recommender Systems (RS) aim at suggesting to users one or several items in which they might have interest. Following the feedback they receive from the user, these systems have to adapt their model in order to improve future recommendations. The repetition of these steps defines the RS as a sequential process. This sequential aspect raises an exploration-exploitation dilemma, which is surprisingly rarely taken into account for RS without contextual information. In this paper we present an explore-exploit collaborative filtering RS, based on Matrix Factor-ization and Bandits algorithms. Using experiments on artificial and real datasets, we show the importance and practicability of using sequential approaches to perform recommendation. We also study the impact of the model update on both the quality and the computation time of the recommendation procedure.
Bi-Objective Optimization of Kidney Exchanges

OF THESIS Bi-Objective Optimization of Kidney Exchanges Matching people to their preferences is an algorithmic topic with real world applications. One such application is the kidney exchange. The best “cure” for patients whose kidneys are failing is to replace it with a healthy one. Unfortunately, biological factors (e.g., blood type) constrain the number of possible replacements. Kidney exchanges seek to alleviate some of this pressure by allowing donors to give their kidney to a patient besides the one they most care about and in turn the donor for that patient gives her kidney to the patient that this first donor most cares about. Roth et al. first discussed the classic kidney exchange problem. Freedman et al. expanded upon this work by optimizing an additional objective in addition to maximal matching. In this work, I implement the traditional kidney exchange algorithm as well as expand upon more recent work by considering multi-objective optimization of the exchange. In addition I compare the use of 2-cycles to 3-cycles. I offer two hypotheses regarding the results of my implementation. I end with a summary and a discussion about potential future work.
Introductory Chapter: Shoulder Joint

Deltoid, one of the strongest muscles in the body, encircles the shoulder joint all around. It provides the shape and bulk to the shoulder joint. It works in almost all the functions of the joint from forward flexion, abduction, and adduction to rotations. It is supplied by the axillary nerve. The pectoralis major and minor, rhomboids, latissimus dorsi, teres major, and trapezius are other major muscles that play an important part in the function and stability of shoulder girdle (Figure 1).
Recognition of Human Activities by Smartphone Sensors Using LSTM Neural Network

Human activities have been a hot research field. Many sensors are embedded in the smartphone, which makes mobile sensor become available. Sensors of smartphone can early get the human activities information, which can analyze the human behaviors and provide the useful information to the human. In this paper, we propose a new method to recognize the human activities, which is based on the LSTM neural network to extract features and classify using accelerometer sensor data and gyroscope sensor data. Experimental results show that using LSTM neural network and TensorFlow deep learning open source architecture to extract motion state characteristics, this method achieves human activities classification with an accuracy of up to 90.4%. Keywords—deep learning; LSTM neural network; activity recognition; sensors of smartphone; TensorFlow
Social Media Enabled E-Participation: Review and Agenda for Future Research

Abstract:This study presents a review of the current state-of-research on e-participation through social media, and classifies the current literature with respect to four broad aspects, namely, stakeholder, theoretical background, context, and research method. Each of these aspects is elaborated with the discussion on benefits and challenges of using social media in e-participation. Further, an integrated conceptual framework is developed by consolidating the major theories identified in the literature. Our analysis indicates that though social media are considered as promising media for promoting e-participation, the benefits of e-participation are not realized yet, owing to the extant challenges. Accordingly, the study proposes five key areas for future research, which are theoretically valuable and practically relevant to both e-participation researchers and policy makers.
Development of Behavior Understanding System based on Object- Human Interaction Pattern Extraction using Deep Learning Model

Human-Robot Interaction (HRI) technology plays a key role in social robotics. The main goal of HRI studies is to develop robots that can communicate with humans through an intuitive interface. In this paper, we focused on the Human Action Understanding (HAU) problem in HRI study fields. We propose an approach for HAU tasks using deep learning models. The proposed system consists of an object-human interaction pattern extraction module and a behaviour goal estimation module. Our main contributions are two-fold: 1) We applied a deep learning method for real-time object detection to extract object-human interaction patterns. 2) We also applied a recurrent neural network to complete the behaviour goal estimation task with a temporal contextual feature data sequence based on object-human interaction patterns. The experimental results from the benchmark data set for human behaviour recognition show that the proposed method performs well.
Segmenting Handwritten Arabic Text

The segmentation and recognition of Arabic handwritten text has been an area of great interest in the past few years. However, a small number of research papers and reports have been published in this area. There are several major problems with Arabic handwritten text processing: Arabic is written cursively and many external objects are used such as dots, ‘Hamza’, ‘Madda’, and diacritic objects. In addition, Arabic characters have more than one shape according to their position inside a word. More than one character can also share the same horizontal space, creating vertically overlapping connected or disconnected blocks of characters. This makes the problem of segmentation of Arabic text into characters, and their classification even more difficult. In this work a technique is presented that segments difficult handwritten Arabic text. A conventional algorithm is used for the initial segmentation of the text into connected blocks of characters. The algorithm then generates pre-segmentation points for these blocks. A neural network is subsequently used to verify the accuracy of these segmentation points. Another conventional algorithm uses the verified segmentation points and segments the connected blocks of characters. These characters can then be used as input to another neural network for classification.
Volume Visualization in Projection-Based Virtual Environments: Interaction and Exploration Tools Design and Evaluation

Examples of volume data include medical scanned data such as CT and MRI data, seismic survey data, and computational fluid dynamic (CFD) data, etc. To better understand volumetric datasets, people use computer hardware and software to manipulate the data and generate 2D projections for viewing; this process is called volume visualization. Much research on volume visualization has been focused on volume rendering (how to render larger sets of data faster with a higher level of realism) or transfer function generation (how to highlight the regions of interest). To help improve the efficiency and efficacy of volume visualization, this research proposed using two different approaches. The first approach is to integrate virtual reality environments (VEs) and human computer interaction (HCI) technologies in volume visualization applications. The second approach is to use various virtual tools that allow users to directly explore and manipulate the volume data in 3D space. A volume visualization system named VRVolVis (Virtual Reality Volumes Visualization System) has been designed and developed to implement these approaches. Many innovations have been integrated into this system, including a fast volume rendering engine, an intuitive HCI paradigm tailored for volume visualization in VEs, and 8 innovative geometric tools that can assist users to fully reveal the internal structure of volumetric datasets. The tools are the clipping plane widget, the data slab widget, the volume probing tool, the volume clipping tool, the regional enhancement tool, the virtual light, the volume eraser and restorer, and the shooting star tool. Two sets of experiments involving 33 participants were conducted, and the experimental results supported the assertion that volume visualization tasks would be performed significant better in VR viewing conditions than Stereo and Conventional conditions, and that using these geometric tools can significantly improve the efficiency and efficacy of the volume visualization process.
Using CVSS to quantitatively analyze risks to software caused by vulnerabilities

Quantitative methods for evaluating and managing software security are becoming reliable with the ever increasing vulnerability datasets. The Common Vulnerability Scoring System (CVSS) provides a way to quantitatively evaluate individual vulnerability. However it cannot be applied to evaluate software risk directly and some metrics of CVSS are hard to assess. To overcome these shortcomings, this paper presents a novel method, which combines the CVSS base score with market share and software patches, to quantitatively evaluate the software risk. It is based on CVSS and includes three indicators: Absolute Severity Value (ASV), Relative Severity Value (RSV) and Severity Value Variation Rate (SVVR). Experimental results indicate that by using these indicators, the method can quantitatively describe the risk level of software systems, and thus strengthen software security.
Learning Analytics For Engineering Education

Engineering faculty should take advantage of data analytics to improve their curriculum, program, and student success. As such, faculty need to strategically conduct the entire data process: knowing the right questions to ask, determining the relevant data to collection, choosing the appropriate instruments to collect those data, analyzing that data, recommending appropriate actions, implementing them, and evaluating the implementation. Furthermore, students need to learn how to analyze data as part of their professional repertoire of design and development intellectual toolkits. To that end, learning analytics practices are detailed in this paper. Engineering education constitute the contextual focus.
Intentional Dialogues: Leveraging Intent to Enable the Effective Reuse of Content

The creation of rich, immersive game worlds is one of the major goals for designers of modern story-based games. The inclusion of unique and interesting dialogues for all of a game’s non-player characters (NPCs), especially the secondary NPCs, does a great deal to increase the believability of the game world being created. Unfortunately, the creation of such dialogues can be very time consuming, and is often considered to be prohibitively expensive. In order to address this problem, I introduce the concept of an Intentional Dialogue that allows designers to efficiently and effectively create multiple variations of a dialogue while ensuring that the variations are appropriate to the NPCs that will be speaking them. I will also introduce several support structures for the Intentional Dialogues and a machine learning classifier that allows designers to quickly populate the system with both new and existing content.