Reviewerly/exHarmony huggingface dataset:
Learning User Preferences via Reinforcement Learning with Spatial Interface Valuing

Interactive Machine Learning is concerned with creating systems that operate in environments alongside humans to achieve a task. A typical use is to extend or amplify the capabilities of a human in cognitive or physical ways, requiring the machine to adapt to the users' intentions and preferences. Often, this takes the form of a human operator providing some type of feedback to the user, which can be explicit feedback, implicit feedback, or a combination of both. Explicit feedback, such as through a mouse click, carries a high cognitive load. The focus of this study is to extend the current state of the art in interactive machine learning by demonstrating that agents can learn a human user's behavior and adapt to preferences with a reduced amount of explicit human feedback in a mixed feedback setting. The learning agent perceives a value of its own behavior from hand gestures given via a spatial interface. This feedback mechanism is termed Spatial Interface Valuing. This method is evaluated experimentally in a simulated environment for a grasping task using a robotic arm with variable grip settings. Preliminary results indicate that learning agents using spatial interface valuing can learn a value function mapping spatial gestures to expected future rewards much more quickly as compared to those same agents just receiving explicit feedback, demonstrating that an agent perceiving feedback from a human user via a spatial interface can serve as an effective complement to existing approaches.
GPU Accelerated Keccak (SHA3) Algorithm

Hash functions like SHA-1 or MD5 are one of the most important cryptographic primitives, especially in the field of information integrity. Considering the fact that increasing methods have been proposed to break these hash algorithms, a competition for a new family of hash functions was held by the US National Institute of Standards and Technology. Keccak was the winner and selected to be the next generation of hash function standard, named SHA-3. We aim to implement and optimize Batch mode based Keccak algorithms on NVIDIA GPU platform. Our work consider the case of processing multiple hash tasks at once and implement the case on CPU and GPU respectively. Our experimental results show that GPU performance is significantly higher than CPU is the case of processing large batches of small hash tasks.
Research on the Teaching and Management Mode of Sino-Foreign Cooperative Education

The Sino-Foreign Cooperative Education (SFCE) is an important mode for some universities adapting to economic globalization, making full use of abroad high-class education resources, and cultivating talents with multiple channels. It has a high-level positive significance on the promotion of modernization of education, expansion of education perspective and enhancement of educational reform and innovation. This paper analyzed the major characters of SFCE mode, discussed the problems existing in the running SFCE projects. Finally, some improvement suggestions had been proposed in this paper.
Calibrated simulation of a NZEB: The Solar Decathlon China 2018 SCUTxPoliTo Prototype

Building model calibration is essential to minimize the gap between the designed and the real energy performance of a NZEB.This paper presents the application of an advanced optimization-based approach to calibrate a real NZEB prototype during its final design and construction phase.It relies on the synergic use of TRNSYS ® , GenOpt ® and Matlab ® .The reached high reliability of the model was used to properly optimize the prototype and create a daily energy planning procedure to match energy production and consumption, maximizing the final score and winning the Solar Decathlon China 2018 competition.
Development of Video Frame Enhancement Technique Using Pixel Intensity Analysis

This paper developed a brightness enhancement technique for video frame pixel intensity improvement. Frames extracted from the six sample video data used in this work were stored in the form of images in a buffer. Noise was added to the extracted image frames to vary the intensity of their pixels so that the pixel values of the noisy images differ from their true values in order to determine the efficiency of the developed technique. Simulation results showed that, the developed technique was efficient with an improved pixel intensity and histogram distribution. The Peak to Signal Noise Ratio evaluation showed that the efficiency of the developed technique for both grayscale and coloured video frames were improved by PSNR of 12.45%, 16.32%, 27.57% and 19.83% over the grey level colour (black and white) for the NAELS1.avi, NAELS2.avi, NTA1.avi and NTA2.avi respectively. Also, a percentage improvement of 28.93% and 31.68% were obtained for the coloured image over the grey level image for Akiyo.avi and Forman.avi benchmark video frame, respectively.
Money in a Human Economy. Keith Hart, ed. New York: Berghahn, 2017. 314 pp.

American EthnologistVolume 46, Issue 1 p. 111-112 Book Review Money in a Human Economy. Keith Hart, ed. New York: Berghahn, 2017. 314 pp. PATRICK NEVELING, University of BernSearch for more papers by this author PATRICK NEVELING, University of BernSearch for more papers by this author First published: 21 January 2019 https://doi.org/10.1111/amet.12741 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Volume46, Issue1February 2019Pages 111-112 RelatedInformation
CPOI: A Compact Method to Archive Versioned RDF Triple-Sets

Large amounts of RDF/S data are produced and published lately, and several modern applications require the provision of versioning and archiving services over such datasets. In this paper we propose a novel storage index for archiving versions of such datasets, called CPOI (compact partial order index), that exploits the fact that an RDF Knowledge Base (KB), is a graph (or equivalently a set of triples), and thus it has not a unique serialization (as it happens with text). If we want to keep stored several versions we actually want to store multiple sets of triples. CPOI is a data structure for storing such sets aiming at reducing the storage space since this is important not only for reducing storage costs, but also for reducing the various communication costs and enabling hosting in main memory (and thus processing efficiently) large quantities of data. CPOI is based on a partial order structure over sets of triple identifiers, where the triple identifiers are represented in a gapped form using variable length encoding schemes. For this index we evaluate analytically and experimentally various identifier assignment techniques and their space savings. The results show significant storage savings, specifically, the storage space of the compressed sets in large and realistic synthetic datasets is about the 8% of the size of the uncompressed sets.
Wait For It: Identifying "On-Hold" Self-Admitted Technical Debt

Self-admitted technical debt refers to situations where a software developer knows that their current implementation is not optimal and indicates this using a source code comment. In this work, we hypothesize that it is possible to develop automated techniques to understand a subset of these comments in more detail, and to propose tool support that can help developers manage self-admitted technical debt more effectively. Based on a qualitative study of 335 comments indicating self-admitted technical debt, we first identify one particular class of debt amenable to automated management: "on-hold" self-admitted technical debt, i.e., debt which contains a condition to indicate that a developer is waiting for a certain event or an updated functionality having been implemented elsewhere. We then design and evaluate an automated classifier which can identify these "on-hold" instances with an area under the receiver operating characteristic curve (AUC) of 0.83 as well as detect the specific conditions that developers are waiting for. Our work presents a first step towards automated tool support that is able to indicate when certain instances of self-admitted technical debt are ready to be addressed.
Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling

We consider the problem of inferring the values of an arbitrary set of variables (e.g., risk of diseases) given other observed variables (e.g., symptoms and diagnosed diseases) and high-dimensional signals (e.g., MRI images or EEG). This is a common problem in healthcare since variables of interest often differ for different patients. Existing methods including Bayesian networks and structured prediction either do not incorporate high-dimensional signals or fail to model conditional dependencies among variables. To address these issues, we propose bidirectional inference networks (BIN), which stich together multiple probabilistic neural networks, each modeling a conditional dependency. Predictions are then made via iteratively updating variables using backpropagation (BP) to maximize corresponding posterior probability. Furthermore, we extend BIN to composite BIN (CBIN), which involves the iterative prediction process in the training stage and improves both accuracy and computational efficiency by adaptively smoothing the optimization landscape. Experiments on synthetic and real-world datasets (a sleep study and a dermatology dataset) show that CBIN is a single model that can achieve state-of-the-art performance and obtain better accuracy in most inference tasks than multiple models each specifically trained for a different task.
A Digital Economy Model for Talent Prediction Data Analytic

This research proposed a model to assist the design of the associated data architecture and data analytic to support talent forecast in the current accelerating changes in economy, industry and business change due to the accelerating pace of technological change. The emerging and re-emerging economy model were available, such as Industrial revolution 4.0, platform economy, sharing economy and token economy. Those were driven by new business model and technology innovation. An increase capability of technology to automate more jobs will cause a shift in talent pool and workforce. New business model emerge as the availabilityand the cost effective emerging technology, and as a result of emerging or re-emerging economic models. Both, new business model and technology innovation, create new jobs and works that have not been existed decades ago. The future workers will be faced by jobs that may not exist today. A dynamics model of inter-correlation of economy, industry, business model and talent forecast were proposed. A collection of literature review were conducted to initially validate the model.
Statistical approach for the optimum selection of photovoltaic modules on a specific site

This paper introduces a probabilistic approach (PA) for the optimum selection of various photovoltaic (PV) modules manufactured by different suppliers to select the most suited module at a specific site location. This approach is based on fitting the probability distributions of the irradiance data measured at a specific hour of a typical day over a long term period (7 years). The goodness of fit test is employed to determine the best fitted probability density function which is then used in the calculation of the average output power and capacity factor (CF) of each module. Thus, the module with the highest average CF over the year is therefore considered as the best suited module for the given site location. The source files of solar irradiance database are approved by the Egyptian Meteorological Authority. Detailed modeling of the PV system characteristics using MATLAB/Simulink has been introduced, which shows complete stepping procedures and respective results. The combination of the detailed PV modeling along with the PA facilitates precise prediction of the best module in terms of output power under varying operating conditions and partial shading for the specified site.
Introducing Gamification Methods To High School Student At Bina Nusantara University Alam Sutera

Exploration studies in this article highlighted approaches that wearable to increase the value of gamification method to examine the impact of the role of facilitator in the idea of the group. Through the activities, the children of high school/high school were invited as respondents. This activity aimed to stimulate creativity to produce a different settlement with the same results. The presentation material and workshops that used materials of paper and stationeries. Finally, this article outlines recommendations for teaching, learning, and apply gamification in decision making. This method is one of the modern methods of teaching and learning. It starts with forming groups, job descriptions, and the active participation of the students and by comparing the characteristics of the method by the way students learn gamification, the positive effects of this method on the educational attainment of students confirmed. Based on the finding of the study the researcher recommended encouraging faculty members to use gamification strategy in teaching, conducting more studies discussing this strategy and its relation to other variables such as critical thinking, conducting more studies on other samples from different study and age levels and from different environments. Keywords: gamification method, student, creativity, university students
Influence Of Knowledge Quality And Team Climate On Team Performance

Nowadays, the success of organizations depends on the capacities of the intangible values rather than tangible assets.Using of knowledge in all business processes is gaining importance in time.For this reason, the acquisition of knowledge, sharing among the members of the organization, development, and management should be seen as a strategic activity in business activity. The aim of this study investigate the role of knowledge quality and team climate on team performance.The data were obtained from a total of 78 employees, 1 senior, 1 middle manager, 10 team managers, 4 project managers, and 62 experts in a private company who operating in the banking sector. The analysis results revealed that there is a positive and significant relationship between knowledge quality and team performance, but there is no significant relationship between team climate and team performance. The findings of this research provide direction of the executives by demonstrating empirical evidence.
DETECTING ABNORMAL ACTIVITIES FROM INPUT VIDEOS AND REPORTING TO AUTHORITIES.

In the context of pattern recognition, abnormal event detection plays an important role. Anomaly detection is an active area of research on its own. Previously known anomaly detection techniques are usually not object based, where objects are not recognized prominently. In this system we find optimized object aware anomaly detection technique, based on certain object categories focusing on mobile objects. Algorithm used performs block based foreground segmentation to restrict our analysis to moving objects and unrelated background dynamics. Object detector is used to discard unrelated objects on connected blocks. Histograms of block-motion trajectories are extracted and cluster them to represent normal events. This framework gives a relatively low computational complexity and high detection accuracy.
AnomiGAN: Generative adversarial networks for anonymizing private medical data

Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence can provide information that can not only identify an individual, but also his or her relatives. Nonetheless, most countries and researchers agree on the necessity of collecting personal medical data. This stems from the fact that medical data, including genomic data, are an indispensable resource for further research and development regarding disease prevention and treatment. To prevent personal medical data from being misused, techniques to reliably preserve sensitive information should be developed for real world application. In this paper, we propose a framework called anonymized generative adversarial networks (AnomiGAN), to improve the maintenance of privacy of personal medical data, while also maintaining high prediction performance. We compared our method to state-of-the-art techniques and observed that our method preserves the same level of privacy as differential privacy (DP), but had better prediction results. We also observed that there is a trade-off between privacy and performance results depending on the degree of preservation of the original data. Here, we provide a mathematical overview of our proposed model and demonstrate its validation using UCI machine learning repository datasets in order to highlight its utility in practice. Experimentally, our approach delivers a better performance compared to that of the DP approach.
For now just happy with the HTML ...

For now just happy with the HTML export, works perfectly. Just gonna push that to production and then remember to export once a week or
Reducing state updates via Gaussian-gated LSTMs

Recurrent neural networks can be difficult to train on long sequence data due to the well-known vanishing gradient problem. Some architectures incorporate methods to reduce RNN state updates, therefore allowing the network to preserve memory over long temporal intervals. To address these problems of convergence, this paper proposes a timing-gated LSTM RNN model, called the Gaussian-gated LSTM (g-LSTM). The time gate controls when a neuron can be updated during training, enabling longer memory persistence and better error-gradient flow. This model captures long-temporal dependencies better than an LSTM and the time gate parameters can be learned even from non-optimal initialization values. Because the time gate limits the updates of the neuron state, the number of computes needed for the network update is also reduced. By adding a computational budget term to the training loss, we can obtain a network which further reduces the number of computes by at least 10x. Finally, by employing a temporal curriculum learning schedule for the g-LSTM, we can reduce the convergence time of the equivalent LSTM network on long sequences.
Degraded Historical Documents Images Binarization Using a Combination of Enhanced Techniques

Document image binarization is the initial step and a crucial in many document analysis and recognition scheme. In fact, it is still a relevant research subject and a fundamental challenge due to its importance and influence. This paper provides an original multi-phases system that hybridizes various efficient image thresholding methods in order to get the best binarization output. First, to improve contrast in particularly defective images, the application of CLAHE algorithm is suggested and justified. We then use a cooperative technique to segment image into two separated classes. At the end, a special transformation is applied for the purpose of removing scattered noise and of correcting characters forms. Experimentations demonstrate the precision and the robustness of our framework applied on historical degraded documents images within three benchmarks compared to other noted methods.
THE CORRELATION BETWEEN MOTIVATION AND ACHIEVEMENT OF SIXTH YEAR PUPILS AT SDN INPRES 3 BIROBULI IN LEARNING ENGLISH

This research was a correlation study with two variables – variable X standing for motivation and variable Y standing for achievement. The purpose of this study was to find out is there any positive correlation between motivation and achievement in leraning English especially for the pupils of grade six at SDN inpres 3 Birobuli. The data were collected by using documentation and questionnaire. Then, they were analyzed descriptively. Through questionnaire and pupils’ learning result, it was found that the level of correlation between variable X and Y was strong enough, there was positive correlation between variable X and Y since the value of r-counted (0,475) was higher than the value of r-table (0,235). Meaning the higher motivation they get the better achievement they have.
Dynamics of students’ ontological reasoning across contexts in modern physics

The ways in which students organize knowledge about entities, such as photons and electrons, (or their ontologies) influence their learning of quantum phenomena.In prior work, we have described different types of dynamic ontologies seen in collective student reasoning; here we extend this analysis to individual written responses to Modern Physics homework, exam, and survey questions about canonical quantum mechanics topics.We present a refined framework that describes unitary, parallel, and blended ontological structures, and then examine students' patterns of use of ontological structures across entities (e.g., photon or electron) and topic areas (double slit, Mach-Zehnder interferometer, and tunneling).Analysis of seven students' responses demonstrate their flexible use of ontologies in individual written work.Through this analysis, we raise questions for future studies about how the wording and framing of the prompts impacts students' ontological reasoning.
KAUdroid - Project Report : Visualizing how Android apps utilize permissions

This paper introduces and implements a web interface to visualize android permission usage with the help of using partial identity attributes. When an app is installed, it asks for permission to us ...
Image Co-Segmentation via Examples Guidance

Given a collection of images which contains objects from the same category, the co-segmentation methods aim at simultaneously segmenting such common objects in each image.Most of existing co-segmentation approaches rely on computing similarities inter-regions representing foregrounds in these images.However, region similarity measurement is challenging due to the large appearance variations among objects in the same category.In addition, for real-world images which have cluttered backgrounds, the existing co-segmentation approaches miss sufficient robustness to extract the common object from the background.In this paper, we propose a new co-segmentation method which takes advantage of the reliable segmentation of few selected images, in order to guide the segmentation of the remaining images in the collection.A random sample of images is first selected from the image collection.Then, the selected images are segmented using an interactive segmentation method.These segmentation results are used to construct positive/negative samples of the targeted common object and background regions respectively.Finally, these samples are propagated to the remaining images in the collection through computing both local and global consistency.The experiments on the iCoseg and MSRC datasets demonstrate the performance and robustness of the proposed method.
Math in our madressahs? – South Asia Citizens Web

It is a fact that children who do not know English, math, or science cannot compete in the job market or benefit from university-level education. They become the victim of conspiracy theories,…