Miracle-dz/UniFAR-FTUs huggingface dataset:
GaitSense: Towards Ubiquitous Gait-Based Human Identification with Wi-Fi

Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi-based gait recognition is more attractive because Wi-Fi infrastructure is almost available everywhere and is able to sense passively without the requirement of on-body devices. However, existing Wi-Fi sensing approaches impose strong assumptions of fixed user walking trajectories, sufficient training data, and identification of already known users. In this article, we present GaitSense , a Wi-Fi-based human identification system, to overcome the above unrealistic assumptions. To deal with various walking trajectories and speeds, GaitSense first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals. On this basis, GaitSense reduces the training efforts in new deployment scenarios by transfer learning and data augmentation techniques. GaitSense also enables a distinct feature of illegal user identification by anomaly detection, making the system readily available for real-world deployment. Our implementation and evaluation with commodity Wi-Fi devices demonstrate a consistent identification accuracy across various deployment scenarios with little training samples, pushing the limit of gait recognition with Wi-Fi signals.
Aspect Extraction for Opinion Mining with a Semantic Model

In this article, we present a semantic model for aspect extraction from Spanish text as part of a complete aspect-based sentiment analysis system. The model uses ontology, semantic similarity, and double propagation techniques to detect explicit and implicit aspects. The proposed approach allows the implementation of a scalable system for any language or domain. The experimental tests were carried out using the SemEval-2016 dataset for task 5, corresponding to the aspect-based sentiment analysis sentence level. The implemented system obtained an F1 score of 73.07 for the aspect extraction, achieving the best results among the systems participating in the comparison, and an F1 score of 89.18 for the hotel domain using a ten-iteration cross-validation.
Flaky test detection in Android via event order exploration

Validation of Android apps via testing is difficult owing to the presence of flaky tests. Due to non-deterministic execution environments, a sequence of events (a test) may lead to success or failure in unpredictable ways. In this work, we present an approach and tool FlakeScanner for detecting flaky tests through exploration of event orders. Our key observation is that for a test in a mobile app, there is a testing framework thread which creates the test events, a main User-Interface (UI) thread processing these events, and there may be several other background threads running asynchronously. For any event e whose execution involves potential non-determinism, we localize the earliest (latest) event after (before) which e must happen. We then efficiently explore the schedules between the upper/lower bound events while grouping events within a single statement, to find whether the test outcome is flaky. We also create a suite of subject programs called FlakyAppRepo (containing 33 widely-used Android projects) to study flaky tests in Android apps. Our experiments on the subject-suite FlakyAppRepo show FlakeScanner detected 45 out of 52 known flaky tests as well as 245 previously unknown flaky tests among 1444 tests.
Research of Real Time Video Monitoring and Remote Control System of Escalators

Abstract. An escalator is a safe related, heavy-duty special equipment. It is a big challenge for escalator manufacturers to detect a dangerous state or the behaviour of passengers on an escalator by using real-time video monitoring thereby helping the administrators in a security monitoring room to control the escalator remotely at the emergent moment. This hasn’t been solved effectively since the escalator was invented 120 years ago. This paper presents some practical methods to overcome the difficulties, which includes following two aspects:
Differentially Private Formation Control: Privacy and Network Co-Design

As multi-agent systems proliferate, there is increasing need for coordination protocols that protect agents' sensitive information while still allowing them to collaborate. Often, a network system and controller are first designed and implemented, and then privacy is only incorporated after that. However, the absence of privacy from the design process can make it difficult to implement without significantly harming system performance. To help address this need, this paper presents a co-design framework for multi-agent networks and private controllers that we apply to the problem of private formation control. Agents' state trajectories are protected using differential privacy, which is a statistical notion of privacy that protects data by adding noise to it. Privacy noise alters the performance of the network, which we quantify by computing a bound for the steady-state error for private formations. Then, we analyze tradeoffs between privacy level, system performance, and connectedness of the network's communication topology. These trade-offs are used to formulate a co-design optimization framework to design the optimal communication topology alongside the optimal privacy parameters for a network running differentially private formation control. Simulation results illustrate the scalability of our proposed privacy/network co-design problem to large multi-agent networks, as well as the high quality of formations one can attain, even with privacy implemented.
Atmospheric observations consistent with reported decline in the UK's methane emissions, 2013–2020

Abstract. Atmospheric measurements can be used as a tool to evaluate national greenhouse gas inventories through inverse modelling. Using eight years of continuous methane (CH4) concentration data, this work assesses the United Kingdom's (UK) CH4 emissions over the period 2013–2020. Using two different inversion methods, we find mean emissions of 2.10 ± 0.09 Tg yr−1 and 2.12 ± 0.26 Tg yr−1 between 2013–2020, and an overall trend of −0.05 ± 0.01 Tg yr−2 and −0.06 ± 0.04 Tg yr−2, a 2–3 % decrease each year. This compares with the mean emissions of 2.23 Tg yr−1 and trend of −0.03 Tg yr−2 (1 % annual decrease) reported in the UK's 2021 inventory between 2013–2019. We examine how sensitive these estimates are to various components of the inversion set-up, such as the measurement network configuration, the prior emissions estimate, the inversion method, and the atmospheric transport model used. We find the decreasing trend to be due primarily to a reduction of emissions from England, which accounts for 70 % of the UK CH4 emissions. Comparisons during 2015 demonstrate consistency when different atmospheric transport models are used to map the relationship between sources and atmospheric observations at the aggregation level of the UK. The posterior annual national means and negative trend are found to be consistent across changes in network configuration. We show, using only two monitoring sites, the same conclusions on mean UK emissions and negative trend would be reached as using the full six-site network, albeit with larger posterior uncertainties. However, emissions estimates from Scotland fail to converge on the same posterior under different inversion setups, highlighting a shortcoming of the current observation network in monitoring all of the UK. Although CH4 emissions in 2020 are estimated to have declined relative to previous years, this decrease is in line with the longer-term emissions trend, and is not necessarily a response to national lockdowns.
Fabrication of New Potentiometric Microsensor for Metformin Based on Modified Screen-Printed Microchip

A new disposable potentiometric micro-senor responsive for metformin drug has been microfabricated using new developed approach. The organic membrane based sensitive layer was prepared by embedded the metformin : tetraphenyl borate ion association complex in plasticized PVC support matrix modified with carbon nanotubes (CNTs). The cocktail coating mixture of the sensitive layer was deposited on the surface of disposable plastic screen-printed microchip using new methodology recently developed. The microfabricated chip assembly was characterized according to IUPAC recommendations as a potentiometric microsensor for analysis of metformin drug. The merits offered by the developed novel disposable microchip include simple, high reliability, good credibility, long life span and low cost and rapid determination of metformin drug. The elaborated microchip was successfully utilized in the quantification of the metformin drug in some pharmaceutical formulations. Statistical calculations were performed to assess the accuracy and precision of the new microchip assembly for accurate determination of the metformin instead of the highly sophisticated expensive machines.
Drowsiness detection by analysis of EEG signal with the help of Machine Learning

Electroencephalogram (EEG) signals give physiological indications of mind whether you are resting or prepared to do some work, think or dream. To understand these things it is essential to aggregate some EEG motion from the human cerebrum. Among all open physiological indications of the cerebrum, EEG is economical and adaptive. The aim of this work is detecting drowsiness of a person by analyzing EEG signal with the help of Machine learning. For this, we examining the EEG signal and we extract the features. Here three different types of features such as time, frequency-based features and sub-band based features are extracted. Total we extract 66 features from each epoch of individual subject in order to classify drowsiness and alertness. Here we considered total 30 persons and we used the Random forest as the classifier techniques to predict the drowsiness states. With these all parameters, with different ratios of active and drowsy training samples our experimental results show more than 95% accuracy.
Detection of Spammer by using Rating Behaviors

Opinion reviews on products are used by potential customers before deciding to purchase a product. Large volumes of reviews are posted to the Internet and detection of fake reviews is now a challenging research problem. Opinion spam or trustworthiness of online opinions is an important issue and this issue has been neglected so far. To the best of our knowledge, there is not many published study on this topic, although Web spam and email spam have been investigated extensively. This paper is presented spammer detection techniques that define the spammer score on rating behaviors of the reviewer. The experiment showed that the presented technique has comparatively effective spammer detection than other techniques based on rating behaviors.
Reliability and Validity of Arabic Translation of the Impact of Event Scale-Revised for COVID-19 Pandemic

Objective: This study aims to translate and validate the Impact of Event Scale-Revised (IES-R) into the Arabic language with a population of medical students at two different universities in Saudi Arabia Method: Fifty-six male and female medical students were tested during different periods of quarantine due to the COVID-19 pandemic using both original and translated versions of the IESR Results: The Arabic IES-R has acceptable internal consistency, with the following alpha coefficients: Intrusion (Cronbach's a= 0 85), Avoidance (Cronbach's a= 0 75), Hyperarousal (Cronbach's a= 0 74), and total IES-R score (Cronbach's alpha= 0 88), all of which were significant (p-value&lt; 0 01) The test-retest reliability of the Arabic scale examined with the same sample is high, with correlation coefficients ranging from 0 75 to 0 81 (p-value&lt; 0 01) for the three subscales and total score The reliability-parallel form method of the Arabic IES-R with the original IES-R indicates excellent consistency between the two scales, with significant (p-value&lt; 0 01) intraclass correlation ranging from 0 80 to 0 89 for its three subclasses and total score Finally, a principal components analysis using Varimax rotation was performed with the 3-factor forced solution explains 50 5% of the variance and generated an intrusion factor (items 1,3,6,9,14,15), avoidance factor (items 5,8,11,17,22), and hyperarousal factor (items 2,4,10,12,16,18,19) Conclusion: The Arabic IES-R has proven to be a reliable and valid measure for posttraumatic stress symptoms in our sample of medical students in quarantine during the COVID-19 pandemic Category: Psychiatry, psychometry, community
Executive Dishonesty and Accounting Irregularities

This paper uses executives’ membership on an extramarital dating website as a proxy for dishonesty, and finds that financial statements from dishonest executives’ firms are more likely to be restated. The likelihood of restatement is higher when dishonest executives are CEOs. Restatements cluster around a few issues, with executive compensation being one of the most outstanding. Dishonest executives also contribute to ineffective internal controls, material weaknesses, and more security lawsuits. When dishonest executives become revealed, their firms’ stock prices decline. The decline is steeper for firms with recent restatement records. In the subsequent year, revealed executives with restatement history are more likely to be demoted to lower positions and receive less compensation.
COMPOSITION OF MAJOR ORGANIC ACIDS IN VEGETABLES AND SPICES

Organic acids are one of the major phytochemicals in vegetables and responsible for food taste and odor. Different organic acids are analyzed in fruits and cereals, but least in vegetables and spices. Organic acids has been analyzed because of their high importance in the formation of other phytochemical and increased antioxidant activity. The aim of the current research was to determine the oxalic, tartaric, quinic, malic, malonic, ascorbic, citric, fumaric, succinic, salicylic and benzoic acid content in fresh and pre-treated (with steam) vegetables and spices using high performance liquid chromatography (HPLC) method. Major organic acids in highest concentrations in spices and vegetables are quinic, malic, malonic and citric acids. Spices contain higher total organic acid content than vegetables. Using steaming as pre-treatment, some of the organic acids content significantly decreased. Obtained changes could be explained by the organic acid formation into more complex chemicals in food or metabolic process. For the future, these changes will be combined with individual phenolic compound changes in analyzed samples.
A Review of Competing Risks Data Analysis

In clinical research, competing risks data are frequently encountered. It occurs when subjects can fail due to multiple causes. For example, in bone marrow transplantation for diseases such as leukemia, time to relapse and death prior to relapse are competing risks. This paper provides a review of statistical methods for competing risks data analysis including the cumulative incidence function and regression models. A real data example will be used to illustrate the methods mentioned in this paper.
iPOSE – Real Time Movement Tracking Application

Real-time analysis of posture detection has been quite challenging and the existing solutions lack accuracy with limited use. In this paper, we present a comparison mechanism for two or more individuals’ bodily actions by detecting their physical movements that have proliferated accuracy. The proposed system should be applicable for automated learning of exercise, sports, dance, yoga, etc. The user would have to provide the system with a video of an individual performing some action that the user wishes to recreate. The system shall perform a comparison between the actions of the user and the expert to point out errors. For the learning process to be more effective we present a technique to modify the orientation of the individual in the video to increase the depth at which the user can go for learning the action.
Using Various Machine Learning Algorithms to Evaluate and Forecast Student Performance of Teamwork in Software Engineering

Teamwork and collaboration are the two main factors for software development methodologies. From previous software engineering works, the students were keen to collaborate in teams for the advancement of their projects and to learn how to use resources which are not only used in the educational environment but also in the industry. The various machine-learning algorithms are used to test student learning from the software engineering cooperation. The analysis of Student Learning of Software Engineering Teamwork prediction methods is performed: Support Vector Machine and K-nearest neighbors. The key objective of this article is to present a comparative analysis to Assess and Predict Student Knowledge in Computer Engineering Teamwork with different exiting classification systems. The experiment performed different classification methods on Data for Software Engineering Teamwork.
Fast SC-Flip Decoding of Polar Codes with Reinforcement Learning

In this paper, we introduce a novel bit-flipping algorithm for fast successive cancellation (FSC) decoding of polar codes. In particular, we first propose a new bit-flipping strategy tailored to single parity-check (SPC) constituent codes of polar codes. A parameterized bit-flipping model is then developed and reinforcement learning (RL) is used to optimize the parameters. Our experimental results show that for a polar code of length 512 with 256 information bits, the proposed decoder has a better or similar error-correction performance compared to the state-of-the-art fast DSCF (FDSCF) decoding algorithm when the same number of maximum decoding attempts is considered.
Dispersion-Corrected Density Functional Theory and Classical Force Field Calculations of Water Loading on a Pyrophyllite(001) Surface

Water adsorption on the (001) surface of pyrophyllite [Al(OH)(Si2O5)] was investigated using density functional theory (DFT) with dispersion corrections and force field calculations. The DFT calculations show that a water molecule can bind either to one or to two basal oxygen atoms of the surface, with adsorption energies varying from −0.10 to −0.19 eV depending on the binding configuration and binding site. Because the water–water interactions are stronger than the water–surface interactions, the energetically preferred structures with two or more molecules on the surface are clusters reminiscent of their gas-phase counterparts. The trend in water–surface binding energies with the number of water molecules obtained from force field calculations qualitatively agrees with that predicted by the dispersion-corrected DFT calculations. However, the force field calculations give a low-energy structural motif with a water molecule coordinated to a hydroxyl group associated with the octahedral layer of the pyroph.
A SURVEY ON: PREDICTION ALGORITHMS FOR ONLINE PRODUCT RECOMMENDATION

As the number of internet users are increasing day by day. This tends for most of the market to move towards online shopping. People are investing much time on social networking sites, so that is the best place to analyze its behavior and show advertisement for the products of their interest. Some of website provides user rating for different product but they do not recommend any user to purchase. So bridging the gap between user social behavior and product requirement is done. In this paper a brief survey of product prediction algorithm is done. Paper has discussed different features for increase the efficiency of the work.
Economies of scale and ecology of scope

Farmers in Uganda grow substantially more crops per unit of land than their counterparts in developing countries. While specialization is often related to economic efficiency, a large ecological literature establishes the positive relationship between species diversity and productivity. Here, we model and estimate the private benefits of crop diversity in order to understand why Ugandan farmers are more diversified than their counterparts in developed countries. Based on empirical facts derived from 65,851 fields, we develop a model that combines the efficiency increasing mechanisms of specialization from the economics literature with economies of scope resulting from ecological interactions and other input complementarities. The model predicts that crop diversity enhances productivity in low input agriculture but also that these private benefits of diversity decline with increasing control over the production environment. We then test these predictions with plot level data from the low input agriculture of Uganda. We find that a 10 percent increase of farm size and a 10 percent increase of labor increases crop diversity by 1.6 and 1.8 percent respectively consistent with the presence of both economies of scale and economies of scope. Holding labor and land fixed, we also find that a 10 percent increase of crop diversity increases revenues by 3 percent, in line with economies of scope. Comparing harvest quantities of crops grown individually and in combination with other crops we show that the economies of scope are likely to result from ecological interactions. Planting a crop in combination with one other crop increases harvest quantities per unit of input by 14 % compared to monocultures. These results suggest that the high levels of crop diversity in ∗For their helpful comments we thank Joséphine Gantois and Eric Strobl as well as participants of the seminars at the Development Economics Seminar at the Vancouver School of Economics, the Environmental Economics Seminar at UBC’s Land and Food Systems, the Conference of the Western Economic Association, the Annual Conference of the Canadian Resource and Environmental Economics Association and the Annual Meeting of the American Economic Association.
Evaluation of Background Radioactivity In Ogbete Coal Mine Dumpsites In Enugu, Nigeria

The assessment of naturally occurring radionuclides 238U, 232Th and 40K in Ogbete coal mine dumpsites in Enugu were carried out using gamma-ray spectrometry with high purity germanium detector to determine the natural radionuclide in the dumpsites and to evaluate the hazards these might have on the public. The calculated average activity concentration of 238U, 232Th and 40K in the samples were 18.7 ± 2.9 Bqkg-1, 24.4 ± 2.2 Bqkg-1, and 164.4 ± 17.3 Bqkg-1 respectively. The mean activity concentration of 238U, 232Th and 40K in the present study is much lower than the world-wide average values of 33 Bqkg-1, 45 Bqkg-1 and 420 Bqkg-1 respectively. The present study revealed that the calculated average absorbed dose rate, indoor annual effective dose rate and outdoor annual effective dose were found as 31.2 nGyh-1, 0.15 mSvy-1 and 0.04 mSvy-1 respectively. The mean values of the absorbed dose rate, the indoor and outdoor annual effective dose rates are much lower than the world average recommended safety limits. The International Commission on Radiological Protection (ICRP) has recommended the annual effective dose equivalent limit of 1mSvy-1for the individual members of the public and 20mSvy-1 for the radiation workers. Therefore it can be concluded that the present study area is radiologically safe from radiation hazards and will pose no harmful effects to the environmental and the living population.
A Survey of Testability Measurements at Various Abstraction Levels

The new EDA tools such as high level automatic synthesis and design analysis programs require measurement of testability at one or more levels of abstraction. Depending on the application and the level of the input hardware description, we may need to measure testability of a design at the gate, RTL or behavioral level. This paper presents a survey of various testability methods at these levels. In the last section of this paper, we compare these methods for their applications, speed and complexity of algorithms.