Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Medical Treatment System Through Nondeterministic Finite Automata

In this article, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to cloud to deliver telemedicine service. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of patient’s illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analysis to evaluate the efficiency.
PartLy

Data partitioning plays a critical role in data stream processing. Current data partitioning techniques use simple, static heuristics that do not incorporate feedback about the quality of the partitioning decision (i.e., fire and forget strategy). Hence, the data partitioner often repeatedly chooses the same decision. In this paper, we argue that reinforcement learning techniques can be applied to address this problem. The use of artificial neural networks can facilitate learning of efficient partitioning policies. We identify the challenges that emerge when applying machine learning techniques to the data partitioning problem for distributed data stream processing. Furthermore, we introduce PartLy, a proof-of-concept data partitioner, and present preliminary results that indicate PartLy's potential to match the performance of state-of-the-art techniques in terms of partitioning quality, while minimizing storage and processing overheads.
Automatic Pet Food Dispenser using Digital Image Processing

The paper has a project design aimed at which pet owner can be able to feed their pet/pets without their presence or Interferences unlike older versions of pet feeder.The motive of our mission is to offer a simpler and extra efficient way for the pet owners to feed their pets, even when they may be now not at domestic and when they are no longer capable of manipulate remotely.The system makes use of Digital Image Processing for implementation.At First, In the project a pet call is provided using a recorded voice through a speaker to indicate feed time of the pet is initiated.The Ultrasonic Sensor is placed in order to detect the pet in front of the system.Once pet detection is done the camera is switched on and Camera captures image of the pet and processes.If the pet is recognized as required pet, a dc motor will be activated to dispense food.The project implemented is for two pets of different species.Hence, we have employed two dc motors to dispense two different kinds of food for two different pets.So, two food containers and two food bowls are provided in this design.Once the required pet if fed successfully, the message will be sent to the owner's mobile number using an API.
THE EFFECT OF TEACHING BY (MOBILE LEARNING) IN UNIVERSITY STUDENTS'S ACHIEVEMENT

There have been efforts to investigate the effect of teaching by using mobile learning in university student's achievement.However, studies examining effect of teaching by using mobile learning in university students are, thus far, rare.This study adopted a quasi-experimental design with two types of teaching methods.One research group was assigned to the mobile learning teaching Method (n=25) and the other to the conventional teaching Method (n= 25).A multiple-choice test was developed in Unit introduces technology in course of pre-vocational education technology, Within the Bachelor of pre-vocational education program at Shobak university college .The results showed that the mobile learning teaching method was more effect than the conventional teaching method in the achievement of university students.
Semi-device-dependent blind quantum tomography

Extracting tomographic information about quantum states is a crucial task in the quest towards devising high-precision quantum devices. Current schemes typically require measurement devices for tomography that are a priori calibrated to high precision. Ironically, the accuracy of the measurement calibration is fundamentally limited by the accuracy of state preparation, establishing a vicious cycle. Here, we prove that this cycle can be broken and the dependence on the measurement device&amp;apos;s calibration significantly relaxed. We show that exploiting the natural low-rank structure of quantum states of interest suffices to arrive at a highly scalable `blind&amp;apos; tomography scheme with a classically efficient post-processing algorithm. We further improve the efficiency of our scheme by making use of the sparse structure of the calibrations. This is achieved by relaxing the blind quantum tomography problem to the de-mixing of a sparse sum of low-rank matrices. We prove that the proposed algorithm recovers a low-rank quantum state and the calibration provided that the measurement model exhibits a restricted isometry property. For generic measurements, we show that it requires a close-to-optimal number of measurement settings. Complementing these conceptual and mathematical insights, we numerically demonstrate that robust blind quantum tomography is possible in a practical setting inspired by an implementation of trapped ions.
Conceptualisation and research of teachers' professional development

The topic of teacher professional development is increasingly gaining momentum in contemporary literature on education. In this paper, we present a possible theoretical conceptualisation of teacher professional development, its characteristics as both individual and collaborative processes, and its correlation with changes in contemporary education. The aim of the research was to identify the professional issues and dilemmas that preoccupy early primary teachers and subject teachers at the beginning of their professional development, as well as which issues and dilemmas they deal with as experienced practitioners, in order to contribute to further conceptualisation of professional development as a continuous process. The findings of the research are in line with the three-phase model of professional development of Fuller and Brown, in which these authors and teachers distinguished the "care for survival" phase, the "care for teaching" phase, and the "student care" phase. The research confirmed the existence of these stages in the professional development of both early primary teachers and subject teachers.
Pupil Detection Based on Oblique Projection Using a Binocular Camera

The real visual attention areas of students during the learning process are vital data.They represent the students' visual concentration and provide strong support for the analysis of learning effects.It is necessary to use professional equipment to collect the visual attention areas of students on a screen but this equipment is expensive, so it cannot be solely depended upon.A binocular camera can directly obtain the depth information under static conditions; thus, detected pupil and iris sizes do not change as the user-to-screen distance changes.Robust pupil detection is an important prerequisite for gaze detection in a real-world setting.Hence, an accurate algorithm to calculate the position of pupils and the size of irises under natural light is proposed in this paper.It is based on the positive and negative oblique projection of relative linear density.The MPIIGaze dataset was used to test the algorithm, and the experimental results show that the algorithm is resistant to changes in illumination and the presence of glasses.Moreover, compared with a system based on a monocular camera, the error in the distance between label points and gaze location is decreased by 5 pixels and dispersion is decreased by 3 pixels.Finally, the line of sight is concentrated and the dispersion is low.With the 'user-screen' distance between 60cm and 80cm, the accuracy can reach up to 1.5 • to 2.2 • .
Protecting Geolocation Privacy of Photo Collections

People increasingly share personal information, including their photos and photo collections, on social media. This information, however, can compromise individual privacy, particularly as social media platforms use it to infer detailed models of user behavior, including tracking their location. We consider the specific issue of location privacy as potentially revealed by posting photo collections, which facilitate accurate geolocation with the help of deep learning methods even in the absence of geotags. One means to limit associated inadvertent geolocation privacy disclosure is by carefully pruning select photos from photo collections before these are posted publicly. We study this problem formally as a combinatorial optimization problem in the context of geolocation prediction facilitated by deep learning. We first demonstrate the complexity both by showing that a natural greedy algorithm can be arbitrarily bad and by proving that the problem is NP-Hard. We then exhibit an important tractable special case, as well as a more general approach based on mixed-integer linear programming. Through extensive experiments on real photo collections, we demonstrate that our approaches are indeed highly effective at preserving geolocation privacy.
Implementation of Incremental Models on Development of Web-Based Loan Cooperative Applications

Technology is an integral part of application development. Utilization of technology under the target user application becomes one assessment in the success of a software. The XYZ Savings and Loan Cooperative is a non-bank financial company engaged in savings and loans. In the employee payroll process so far it still uses the manual method, start from recording attendance, up to the salary calculation process. Web-based application development developed by researchers is used to overcome the problems of XYZ Savings and Credit Cooperatives. Development of Savings and Loan Cooperative applications developed using the incremental model. 31 respondents from the company tested the application. Application testing based on usability factor ISO 9126-3. Applications get a value of 82.66% in terms of ease of understanding, 81.50% in terms of ease of study, 81% in terms of ease of operation, 81.33% in terms of interest and 82% in terms of usage compliance. The average total rating in terms of the usefulness of the application get 82.66% so the application is very useful for companies in helping to calculate employee payroll.
An overview of blockchain science and engineering

This is the preface to a special issue in the journal Royal Society Open Science, themed around blockchain technology. Since this is still an emergent and interdisciplinary field, we first provide a gentle introduction into that larger topic. Then, we discuss why this technology has been criticized for not being energy-efficient. Next, we provide an analysis of recent developments in blockchain research that may help with making blockchain technology truly sustainable. Finally, we highlight some of the contributions made by papers in this special issue.
Point-Symmetric Extension-Based Interval Shannon-Cosine Spectral Method for Fractional PDEs

The approximation accuracy of the wavelet spectral method for the fractional PDEs is sensitive to the order of the fractional derivative and the boundary condition of the PDEs. In order to overcome the shortcoming, an interval Shannon-Cosine wavelet based on the point-symmetric extension is constructed, and the corresponding spectral method on the fractional PDEs is proposed. In the research, a power function of cosine function is introduced to modulate Shannon function, which takes full advantage of the waveform of the Shannon function to ensure that many excellent properties can be satisfied such as the partition of unity, smoothness, and compact support. And the interpolative property of Shannon wavelet is held at the same time. Then, based on the point-symmetric extension and the general variational theory, an interval Shannon-Cosine wavelet is constructed. It is proved that the first derivative of the approximated function with this interval wavelet function is continuous. At last, the wavelet spectral method for the fractional PDEs is given by means of the interval Shannon-Cosine wavelet. By means of it, the condition number of the discrete matrix can be suppressed effectively. Compared with Shannon and Shannon-Gabor wavelet quasi-spectral methods, the novel scheme has stronger applicability to the shockwave appeared in the solution besides the higher numerical accuracy and efficiency.
Graduation Prediction System On Students Using C4.5 Algorithm

Bumigora University College there are several things that are not balanced between the entry and exit of students who have completed their studies. Students who enter in large numbers, but students who graduate on time below the specified standards. As result, there was a huge accumulation of students in each graduation period. One solution to overcome the problem above needs a data mining based system in monitoring or utilizing student development in predicting graduation using the C4.5 algorithm. The stages of this research began with problem analysis, data collection, data requirement analysis, data design, coding, and testing. The results of this study are the implementation of the C4.5 algorithm for predicting student graduation on time or not. The data used is the data of students who have graduated from 2010 to 2012. The level of acceptance generated using the confusion matrix is ​​93,103% accuracy using 163 training data and 29 testing data or 85% training data and 15% testing data. The results of research and testing that has been done, C4.5 algorithm is very suitable to be used in student graduation prediction.
Development Synonym Set for the English Wordnet Using the Method of Comutative and Agglomerative Clustering

Wordnet is a collection of words that interpret or present a meaning, in its development Wordnet has an important part, the Synonym Set or Synset. In making Synonym sets, synonyms are needed and the commutative nature of words is needed. To get word synonyms, the English language thesaurus becomes the reference data for taking synonym data. Broadly speaking, the difference between Wordnet and the dictionary is that the meaning of the word is related to other words, to determine the equation requires a commutative process. The process is made easy by using commutative methods that will produce a candidate synonym set. Candidates for the synonym set cannot be used for word syntax, the grouping process of words which produces the Synonym set as the final result must be carried out. The process of grouping words can one of them use clustering techniques, in this study will use Agglomerative Clustering techniques. In the process of agglomerative clustering techniques there is a threshold value to determine the number of repetitions or as a condition to stop the iteration process. The clustering process in this study will use a threshold value of 0.1 to 1 to test the best threshold value to produce the best Synonym set and calculate its accuracy value. Accuracy calculation and evaluation will use the F-measure method to find the best results.
SecACS: Enabling lightweight secure auditable cloud storage with data dynamics

With the popularity of cloud computing and IoT, consistently increasing data brings us a great burden to store locally. Due to the local limited storage capacity and expensive maintenance cost, individuals and enterprises are preferred to outsource their data to the public commercial cloud (e.g., Google Drive and Dropbox). However, the commercial cloud can not be fully trusted because data on the cloud may be modified by various reasons (e.g., outside hackers' attacks, hardware or software failures). To ensure data integrity, one of the most popular techniques is secure cloud storage (SCS). Nevertheless, traditional solutions of SCS with heavy cryptographic operations make data outsourcing computationally complicated. Moreover, most proposed approaches only focus on verifying data integrity while neglecting the functionality of data dynamics. To address these issues, we propose a Secure Auditable Cloud Storage (SecACS) scheme supporting data dynamics. SecACS requires less computation time by employing lightweight cryptographic operations. Compared to the previous solutions, the extensive experimental results demonstrate that SecACS achieves one order of magnitude faster in outsourcing data and twice faster in checking data integrity.
A Span-based Linearization for Constituent Trees

We propose a novel linearization of a constituent tree, together with a new locally normalized model.For each split point in a sentence, our model computes the normalizer on all spans ending with that split point, and then predicts a tree span from them.Compared with global models, our model is fast and parallelizable.Different from previous local models, our linearization method is tied on the spans directly and considers more local features when performing span prediction, which is more interpretable and effective.Experiments on PTB (95.8 F1) and CTB (92.1 F1) show that our model significantly outperforms existing local models and efficiently achieves competitive results with global models.
Density-adaptive kernel based efficient reranking approaches for person reidentification

Person reidentification (ReID) refers to the task of verifying the identity of a pedestrian observed from nonoverlapping views in a surveillance camera network. It has recently been validated that reranking can achieve remarkable performance improvements in person ReID systems. However, current reranking approaches either require feedback from users or suffer from burdensome computational costs. In this paper, we propose to exploit a density-adaptive smooth kernel technique to achieve efficient and effective reranking. Specifically, we adopt a smooth kernel function to formulate the neighbor relationships among data samples with a density-adaptive parameter. Based on this new formulation, we present two simple yet effective reranking methods, termed inverse density-adaptive kernel based reranking (inv-DAKR) and bidirectional density-adaptive kernel based reranking (bi-DAKR), in which the local density information in the vicinity of each gallery sample is elegantly exploited. Moreover, we extend the proposed inv-DAKR and bi-DAKR methods to incorporate the available extra probe samples and demonstrate that when and why these extra probe samples are able to improve the local neighborhood and thus further refine the ranking results. Extensive experiments are conducted on six benchmark datasets, including: PRID450s, VIPeR, CUHK03, GRID, Market-1501 and Mars. The experimental results demonstrate that our proposals are effective and efficient.
Balancing Individual Preferences and Shared Objectives in Multiagent Reinforcement Learning

In multiagent reinforcement learning scenarios, it is often the case that independent agents must jointly learn to perform a cooperative task. This paper focuses on such a scenario in which agents have individual preferences regarding how to accomplish the shared task. We consider a framework for this setting which balances individual preferences against task rewards using a linear mixing scheme. In our theoretical analysis we establish that agents can reach an equilibrium that leads to optimal shared task reward even when they consider individual preferences which aren't fully aligned with this task. We then empirically show, somewhat counter-intuitively, that there exist mixing schemes that outperform a purely task-oriented baseline. We further consider empirically how to optimize the mixing scheme.
APLIKASI PEMILIHAN KARYAWAN TERBAIK DENGAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) (STUDI KASUS CITRA WIDYA TEKNIK)

The Best Employee Selection application is part of the decision support system.Citra Widya Teknik was used as a place of research, because in the evaluation of performance and selection of the best employees still use the observation system, which of course this requires a long time and the assessment becomes less objective.This assessment has 4 criteria: Responsibility, Job Knowledge, Cooperation, Quality of Work using the method Simple Additive Weighting (SAW).The research methods used in this study were interviews, observation and literature study.The system development method uses the Waterfall method, and the application is designed using the PHP programming languange and MySQL database.From the test calculation, the data accuracy is 100%, namely manual calculation and application calculation results are as expected.The results of this study resulted in an application for selecting the best employees to use for awarding/rewards.
Optimization of conditions to convert phosphogypsum into Ca(OH)2 and Na2SO4

With a view to finding solutions to the problems of Moroccan phosphogyosum (PG) in the long term, a research theme has been developed in our laboratory on the valorization of this residue by its conversion into marketable products by soft chemistry way less expensive. Thus, we investigated the optimal experimental conditions allowing a good efficiency of PG conversion by using NaOH to synthesize marketable products such as Ca(OH) 2 and Na 2 SO 4 of high purity. We varied the reagent concentrations and the attack durations to optimize these conditions. The quality of the products obtained is checked by XRD, IR and ICP to determine the distribution of PG impurities between obtained products. The morphology and size distribution were also established by SEM and laser granulometry.
Hybrid Learning for Multi-agent Cooperation with Sub-optimal Demonstrations

This paper aims to learn multi-agent cooperation where each agent performs its actions in a decentralized way. In this case, it is very challenging to learn decentralized policies when the rewards are global and sparse. Recently, learning from demonstrations (LfD) provides a promising way to handle this challenge. However, in many practical tasks, the available demonstrations are often sub-optimal. To learn better policies from these sub-optimal demonstrations, this paper follows a centralized learning and decentralized execution framework and proposes a novel hybrid learning method based on multi-agent actor-critic. At first, the expert trajectory returns generated from demonstration actions are used to pre-train the centralized critic network. Then, multi-agent decisions are made by best response dynamics based on the critic and used to train the decentralized actor networks. Finally, the demonstrations are updated by the actor networks, and the critic and actor networks are learned jointly by running the above two steps alliteratively. We evaluate the proposed approach on a real-time strategy combat game. Experimental results show that the approach outperforms many competing demonstration-based methods.