Miracle-dz/UniFAR-FTUs huggingface dataset:
A non-intrusive system for seated posture identification

In this contribution a system for seated posture identification is presented. The assessment tools is based on an office-chair equipped with sensors. In more details, a set of textile pressure sensors has been placed on a chair both on the chair backrest and on the seat. The position of the sensors has been selected for maximizing the possibility of sensing minimum variations of the subject’s posture. To validate the system, an extensive subjective experiment has been performed in which the subject undergoes an increasing stress-level test. The collected results show that this instrument is effective in assessing the attention/fatigue of a subject in seating condition by the analysis of body posture.
SMGuard: A Flexible and Fine-Grained Resource Management Framework for GPUs

GPUs have been becoming an indispensable computing platform in data centers, and co-locating multiple applications on the same GPU is widely used to improve resource utilization. However, performance interference due to uncontrolled resource contention severely degrades the performance of co-locating applications and fails to deliver satisfactory user experience. In this paper, we present SMGuard, a software approach to flexibly manage the GPU resource usage of multiple applications under co-location. We also propose a capacity based GPU resource model CapSM, which provisions the GPU resources in a fine-grained granularity among co-locating applications. When co-locating latency-sensitive applications with batch applications, SMGuard can prevent batch applications from occupying resources without constraint using quota based mechanism, and guarantee the resource usage of latency-sensitive applications with reservation based mechanism. In addition, SMGuard supports dynamic resource adjustment through evicting the running thread blocks of batch applications to release the occupied resources and remapping the uncompleted thread blocks to the remaining resources, which avoids the relaunch of the preempted kernel. The SMGuard is a pure software solution that does not rely on special GPU hardware or programming model, which is easy to adopt on commodity GPUs in data centers. Our evaluation shows that SMGuard improves the average performance of latency-sensitive applications by 9.8× when co-located with batch applications. In the meanwhile, the GPU utilization can be improved by 35 percent on average.
Association Between Triglyceride Level and Glycemic Control Among Insulin-Treated Patients With Type 2 Diabetes

CONTEXT Elevated blood triglyceride levels are known to increase the risks of diabetes and prediabetes. However, it is still unclear whether elevated triglyceride levels are associated with inadequate glycemic control in patients with type 2 diabetes mellitus. OBJECTIVE To investigate the association between elevated triglyceride levels and inadequate glycemic control among insulin-treated patients with type 2 diabetes mellitus. DESIGN, SETTING, AND PATIENTS We recruited 20,108 patients with type 2 diabetes mellitus who were treated with a sufficient dose of insulin. These patients were from the 2013 China National HbA1c Surveillance System study conducted in Mainland China. Multivariate logistic regressions were used to assess the association of triglyceride level with inadequate glycemic control. RESULTS Overall, 56.0% of the subjects had elevated triglyceride levels (≥1.70 mmol/L); prevalence of HbA1c ≥7.0% (53 mmol/mol) and ≥6.5% (48 mmol/mol) was 67.2% and 83.4%, respectively. The adjusted ORs (95% CIs) of HbA1c ≥7.0% were 1.06 (0.98, 1.15), 1.35 (1.23, 1.48), and 3.12 (2.76, 3.53) for those with triglyceride levels in ranges of 1.70 to 2.29, 2.30 to 3.39, and ≥3.40 mmol/L, respectively, compared with those with triglyceride levels of <1.70 mmol/L. There was a similar association between triglyceride levels and HbA1c ≥6.5%. This association was confirmed by subgroup analyses. There was also a strong nonlinear dose-response relationship between triglyceride level and inadequate glycemic control. CONCLUSIONS Elevated triglyceride levels were strongly associated with inadequate glycemic control; thus, suppressing triglyceride levels may attain more optimal glycemic control in patients with type 2 diabetes mellitus.
DPS - Printer: Dual Printer System

A Dual printing system is a mechanism used in normal printer to reutilize not only print medium but also print media. This mechanism involves three different procedures which works according to sensors used in it. A waste paper sheet full of texts or images or scratches, is given as an input to the printer to erase all the rough text and reutilize the waste page. These texts or images or scratches are done by using pen or pencil or printer text. Also we can abstract and reuse printed text ink obtained during the Heating procedure. In this whole system three procedures are involved. In the first process, pencil made rough work on the paper/waste paper erased by using erasable toner. In second procedure normal printer ink made or ball pen made texts and images erased using heating mechanism. Printed text ink melted during heating procedure is fused by applying cold air through head2. After that fused ink particles are abstracted and collected in the black ink container and same container can be reused for next printing in the printer. If texts or images are not erased during erasable toner and heating process then third technique (white ink spreading) is applied over the pixels of the character or pattern to get blank sheet. By applying all above techniques printing medium and print media are reutilized without affecting paper quality to save paper cost, printer ink, carbon emission and environment.
A Review for Weighted MinHash Algorithms

Data similarity (or distance) computation is a fundamental research topic which underpins many high-level applications based on similarity measures in machine learning and data mining. However, in large-scale real-world scenarios, the exact similarity computation has become daunting due to “3V” nature (volume, velocity and variety) of big data. In this case, the hashing techniques have been verified to efficiently conduct similarity estimation in terms of both theory and practice. Currently, MinHash is a popular technique for efficiently estimating the Jaccard similarity of binary sets and furthermore, weighted MinHash is generalized to estimate the generalized Jaccard similarity of weighted sets. This review focuses on categorizing and discussing the existing works of weighted MinHash algorithms. In this review, we mainly categorize the weighted MinHash algorithms into quantization-based approaches, “active index”-based ones and others, and show the evolution and inherent connection of the weighted MinHash algorithms, from the integer weighted MinHash ones to the real-valued weighted MinHash ones. Also, we have developed a Python toolbox for the algorithms, and released it in our github. We experimentally conduct a comprehensive study of the standard MinHash algorithm and the weighted MinHash ones in the similarity estimation error and the information retrieval task.
Deep Face Quality Assessment

Face image quality is an important factor in facial recognition systems as its verification and recognition accuracy is highly dependent on the quality of image presented. Rejecting low quality images can significantly increase the accuracy of any facial recognition system. In this project, a simple approach is presented to train a deep convolutional neural network to perform end-to-end face image quality assessment. The work is done in 2 stages : First, generation of quality score label and secondly, training a deep convolutional neural network in a supervised manner to predict quality score between 0 and 1. The generation of quality labels is done by comparing the face image with a template of best quality images and then evaluating the normalized score based on the similarity.
Evaluation of Use Agent Proposal System Using Unified Theory of Acceptance and User Technology (UTAUT) Model

The life insurance company rapidly growing because the demand of life insurance is increasing now days. With more competitor in life insurance industry, company need to have appropriate, attractive and competitive insurance product for their customer. Company employee who offers insurance product directly to customer is called insurance agent. Those competitive environments make company to equipped agent with mobile application, which called agent proposal system. With agent proposal system, agent can give an interactive presentation to customer, allow them to make simulation, and offer insurance product to customer. In order to enhance/improve the application, company need to evaluate the use of the agent proposal system. The evaluation result is expected to give company the conclusion about factor that influence agent to increase the use of agent proposal system and as suggestion to enhance/improve the system. The evaluation use UTA UT model with variable used are social influence, performance expectancy, facilitating condition, behavior intention, effort expectancy, use behavior and moderator: experience and age. The sample is 217 agents and the data collection using questionnaire. The result show that performance expectancy, effort expectancy and social influence have a positive influence to behavior intention and behavior intention and facilitating condition have a positive influence to use behavior of agent proposal system.
Gauges, loops, and polynomials for partition functions of graphical models

Graphical models represent multivariate and generally not normalized probability distributions. Computing the normalization factor, called the partition function, is the main inference challenge relevant to multiple statistical and optimization applications. The problem is #P-hard that is of an exponential complexity with respect to the number of variables. In this manuscript, aimed at approximating the partition function, we consider multi-graph models where binary variables and multivariable factors are associated with edges and nodes, respectively, of an undirected multi-graph. We suggest a new methodology for analysis and computations that combines the Gauge function technique from Chertkov and Chernyak (2006 Phys. Rev. E 73 065102; 2006 J. Stat. Mech. 2006 P06009) with the technique developed in Anari and Oveis Gharan 2017 arXiv:1702.02937; Gurvits 2011 arXiv:1106.2844; Straszak and Vishnoi 2017 55th Annual Allerton Conf. on Communication, Control, and Computing, based on the recent progress in the field of real stable polynomials. We show that the Gauge function, representing a single-out term in a finite sum expression for the partition function which achieves extremum at the so-called belief-propagation gauge, has a natural polynomial representation in terms of gauges/variables associated with edges of the multi-graph. Moreover, Gauge function can be used to recover the partition function through a sequence of transformations allowing appealing algebraic and graphical interpretations. Algebraically, one step in the sequence consists of the application of a differential operator over gauges associated with an edge. Graphically, the sequence is interpreted as a repetitive elimination/contraction of edges resulting in multi-graph models on decreasing in size (number of edges) graphs with the same partition function as in the original multi-graph model. Even though the complexity of computing factors in the sequence of the derived multi-graph models and respective Gauge functions grow exponentially with the number of eliminated edges, polynomials associated with the new factors remain bi-stable if the original factors have this property. Moreover, we show that BP estimations in the sequence do not decrease, each low-bounding the partition function.
Resonant Frequency Analysis for Rectangular Waveguide Loaded with Anisotropic Dielectric Material Using 3D-FDTD Method

This paper deals with the resonant frequency analysis based on three dimension (3D) finite-difference time-domain (FDTD) method for rectangular waveguide which is loaded with anisotropic dielectric material. The method has been implemented to analyze and solve a wide variety of electromagnetics (EM) problems. The rectangular waveguide with the cross-section of $40\mathbf{mm}\times 20\mathbf{m}$ and loaded with anisotropic dielectric material is modeled as numerical expression of magnetic and electric fields in which the interaction of fields are analyzed using 3D-FDTD method. The results of analysis in terms of EM wave and mode that appear in the waveguide are then validated theoretically. Furthermore, similar analyses using 3D-FDTD method are also applied for an hollow rectangular waveguide as well as for a rectangular waveguide which is loaded with isotropic dielectric material. The analysis results demonstrate good agreements with the theoretical predictions, although there were 0.26-2.32% discrepancies occurred in the 3D-FDTD method.
Analyzing the health condition and chemical degradation in field aged transformer insulation oil using spectroscopic techniques

Power transformers are the most essential components of electrical power transmission and distribution networks. During the course of their operation, transformers are subjected to huge electrical and thermal stresses due to which insulation oil inside the transformer is highly degraded. The degradation of insulation oil leads to catastrophic transformer failure which further leads to power system network failure. Hence proper inspection and regular examination of transformer insulation oil is required for smooth and reliable operation of power system. In this research work, insulation oil samples from in-service transformers were taken and analyzed and the results were compared with same grade fresh mineral oil. Absorption spectroscopy techniques e.g. Fourier Transform Infrared Spectroscopy (FTIR) and UV-Visible Spectroscopy were adopted to analyze the insulation oil degradation. The outcome of absorption of aged transformer oil is so significant that absorption increased by observable margin over a wide range of wavelengths. Chemical characterization results highlight that huge amounts of impurities are added to the insulation oil that tend to highly degrade the oil and decrease useful service life of transformer. Hence a proper inspection of the most important part of transformer i.e. insulation oil is required on regular basis.
Determining Fake Statements Made by Public Figures by Means of Artificial Intelligence

This paper shows an approach for detecting fake statements made by public figures by means of artificial intelligence. Several approaches were implemented as a software system and tested against a data set of statements. The best achieved result in binary classification problem (true or false statement) is 86%. The results may be improved in several ways that are described in the article as well.
Adaptive Network Coding Based Cooperative Medium Access for Wireless Networks

Ever since the introduction of network coding (NC) technique, several Medium Access Control (MAC) protocols based on NC have been developed for wireless networks of three node topology as well as ‘X’ topology, considering that the information flows originate from the source (or reaches the relay) simultaneously. However, there are cases when these information flows do not occur simultaneously. In this context, a new medium access mechanism which is a modified version of CSMA/CA based IEEE 802.11 protocol is proposed in this paper, considering ‘X’ topology based wireless network. It consists of two independent sources, transmitting to the same pair of destinations with the help of a relay node. The relay node of an ongoing cooperative transmission checks for NC opportunity before forwarding the packet to the destinations. If NC opportunity exists, then the relay initiates packet exchange with the other potential source. If not, it continues with the procedures of existing protocol. A suboptimal method of relay selection is also discussed. The proposed method is analyzed in terms of throughput and packet delay and the performance is compared with that of IEEE 802.11 based cooperative MAC protocol. Results show that the proposed protocol shows an improved performance.
Stacked Autoencoder Networks Based Speaker Recognition

Speech signals convey a variety of mixed information, from language to speaker-specific information. However, the information may prevent the speech or speaker recognition system from producing better performance. By unsupervised pre-training and supervised fine-tuning, the deep neural network based on autoencoders can effectively extract critical information. In this paper, we propose a hybrid model combining Stacked Autoencoder and mel-frequency cepstral coefficients (MFCCs) to improve the system performance. The experimental results show that the accuracy of speaker recognition system in TIMIT corpus achieve an improvement which is up to 93.3% by the combination of deep learning network and MFCC features, especially in female.
Use of Autobiographical Memory for Enhancing Adaptive HRI in Multi-User Domestic Environment

The use of social robots in the domestic environment has increased during the past few decades. These robots are intended to maintain long-term interactions with humans while involving in a variety of tasks including daily activities, entertainment, and assisting elderly or disabled people. The ability to learn user's preferences and adapting interaction accordingly is a must for such robots. As the domestic environment consists of non-experts, social robots must possess more natural and human-friendly interaction capabilities. This paper presents an Autobiographical Memory(AM) based intelligent system which can learn user preferences through natural interactions and provide user adaptive services for each user in the multi-user domestic environment. The system is capable of learning user's preferences from hislher own statements and from another person's statements. Furthermore, the system is capable of adapting to user's hidden preference and changes of preferences easily. The robot's memory has been structured in such a way that it can easily remember the user groups and the relationship between users. This facilitates the robot for learning preferences which are common to a group of users. The system has been tested and validated using snack and beverage suggestion scenario.
Problems of Regionalization in the Light of the Schengen Zone

Globalization is becoming increasingly challenging in the 21st century. Processes that go beyond the horizontal relations of the basic actors of international relations – states – become multifaceted. The Schengen area is an example of a new regional structure (though still not global). Based on the analysis of both EU activities and documents produced by the EC, the author points to the current limitations and potential possibilities of the Brexit Schengen Area. In the author’s opinion, only further extension of the Schengen Area, with equal treatment of all its members, can overcome the deadlock which is relevant to the European Union and thus to a large extent the Schengen Area.
Characterization of MPI Usage on a Production Supercomputer

MPI is the most prominent programming model used in scientific computing today. Despite the importance of MPI, however, how scientific computing applications use it in production is not well understood. This lack of understanding is attributed primarily to the fact that production systems are often wary of incorporating automatic profiling tools that perform such analysis because of concerns about potential performance over-heads. In this study, we used a lightweight profiling tool, called Autoperf, to log the MPI usage characteristics of production applications on a large IBM BG/Q supercomputing system (Mira) and its corresponding development system (Cetus). Autoperf limits the amount of information that it records, in order to keep the overhead to a minimum while still storing enough data to derive useful insights. MPI usage statistics have been collected for over 100K jobs that were run within a two-year period and are analyzed in this paper. The analysis is intended to provide useful insights for MPI developers and network hardware developers for their next generation of improvements and for supercomputing center operators for their next system procurements.
A Dynamic Edge Caching Framework for Mobile 5G Networks

Mobile edge caching has emerged as a new paradigm to provide computing, networking resources, and storage for a variety of mobile applications. That helps achieve low latency, high reliability, and improve efficiency in handling a very large number of smart devices and emerging services (e.g., IoT, industry automation, virtual reality) in mobile 5G networks. Nonetheless, the development of mobile edge caching is challenged by the decentralized nature of edge nodes, their small coverage, limited computing, and storage resources. In this article, we first give an overview of mobile edge caching in 5G networks. After that, its key challenges and current approaches are discussed. We then propose a novel caching framework. Our framework allows an edge node to authorize the legitimate users and dynamically predicts and updates their content demands using the matrix factorization technique. Based on the prediction, the edge node can adopt advanced optimization methods to determine optimal content to store so as to maximize its revenue and minimize the average delay of its mobile users. Through numerical results, we demonstrate that our proposed framework provides not only an effective caching approach, but also an efficient economic solution for the mobile service provider.
Streaming Hardness of Unique Games

We study the problem of approximating the value of a Unique Game instance in the streaming model. A simple count of the number of constraints divided by $p$, the alphabet size of the Unique Game, gives a trivial $p$-approximation that can be computed in $O(\log n)$ space. Meanwhile, with high probability, a sample of $\tilde{O}(n)$ constraints suffices to estimate the optimal value to $(1+\epsilon)$ accuracy. We prove that any single-pass streaming algorithm that achieves a $(p-\epsilon)$-approximation requires $\Omega_\epsilon(\sqrt{n})$ space. Our proof is via a reduction from lower bounds for a communication problem that is a $p$-ary variant of the Boolean Hidden Matching problem studied in the literature. Given the utility of Unique Games as a starting point for reduction to other optimization problems, our strong hardness for approximating Unique Games could lead to down\emph{stream} hardness results for streaming approximability for other CSP-like problems.
Gamification for Learning Basic Algorithm

Intrinsic motivation is essential in learning programming. Gamification can be used to increase the intrinsic motivation of students in the learning process. Gamification is a technique of applying game-elements into learning activities. A drawback of gamification is that it is one-size-fit-all, which means that the same gamification element is applied to all students. Hence, it is necessary to apply adaptation that consider the characteristics of students, so that students can get learning materials that suit their needs and abilities. In our research, we have combined adaptive navigation, in the forms of direct guidance, link hiding, link annotation and link generation, and gamification for learning basic algorithm. The results indicate that our approach can increase the intrinsic motivation of students.