Reviewerly/exHarmony huggingface dataset:
USING ENGLISH POP SONG TO IMPROVE STUDENTS’ LISTENIG SKILL

The obejective of study was to to find out that teaching listening skill by using English pop song can improve student’s listening skill. This research conducted at the first grade students of SMA Negeri 1 Glumpang Tiga. The sample of this study were X mia 1 class  as experimental and X mia 2 as control class. To find the data, the researcher did experimental teaching for six meetings. The insrument used to collected the data in this research was a set of writen test, consisted of pre-test and post-test. It was aimed to find out the improvement of the students’ skill in mastering listening before and after using English pop song. The data show that, the mean score of pre-test the experimental class was 27,7 while the mean score of pre-test in control class was 27,7. The mean of score of post-test in experimental class was 53,3 while the mean score of post-test in control class was 44,4. Bised that the result of t-test in experimental class was 12, 5 while t-test in control class was 7,52. In this research null hypotesis was rejected and alternative hypothesis was accepted. It mean that the using English pop song in teaching listening skill significantly improve the students’ listening skill. . Keywords :  Listening skill,English Pop song .
Modelling the subjective perspective in requirement elicitation meetings : An exploratory case study investigating the communicative problems in requirement elicitation meetings in the light of metaphorisation

The following research presents a study of communication in requirement elicitation meetings. Achieving consensus of requirements is difficult in mostsituations, but even more so in requirement eli ...
High-dimensional temporal mode propagation in a turbulent environment

Temporal modes of photonic quantum states, intrinsically possess high dimensional Hilbert spaces, provide a new framework to develop a robust free-space quantum key distribution (QKD) scheme in a maritime environment. We show that the high-dimensional temporal modes can be used to fulfill a persistent communication channel to achieve high photon-efficiency even in severe weather conditions. We identify the parameter regimes that allow for a high-fidelity quantum information transmission. We also examine how the turbulent environment affects fidelity and entanglement degree in various environmental settings.
A Novel Approach to Security System of Multimedia Contents Protection for Cloud Computing

The planned multimedia system Content Protection System For Cloud Computing, novel approach for securing a multimedia system contents. The system supports a value potency, quick development, deployment, measurability and snap for accomplishment feat on a cloud infrastructure this method will used a for shielding a special forms of transmission contents variety of a audio file, 2D video, 3D Video, images, songs, music files. Achieving a security system follows a combine of levels 1) Creates a signatures of a 3D videos 2) Distributed matching Engine for transmission objects. for each transmission objects a separate signature square measure planning to be created and this method creates a durable and representative signatures for the 3D Videos ,that checks the content by content to hunt out modified copy .The second level Distributed matching Engine have a high quality and it's designed to support for various types of multimedia system objects. Of 3D videos, whereas our system detects quite ninety eight of them. This comparison shows the need for the planned 3D signature technique, since the progressive industrial system wasn't able to handle 3D videos The system can run on personal clouds, public clouds, or any combination of public-private clouds projected system is ascendable and value effective and relates to the detection of copied and changed material practice cloud systems, and a lot of considerably to a system and in an internet setting for the detection of duplicated, copyright material.
Process development for BAUHAUS WEB´s warehouse managed by PostNord TPL at increased customer order management

PostNord TPL manages the warehouse for BAUHAUS WEB, which has expanded since the start in 2011. PostNord TPL face the challenge of handling articles with different characteristics from BAUHAUS WEB' ...
Interactive Machine Learning Approach for Staff Selection Using Genetic Algorithm

Machine learning is meant to extract knowledge from vast data. It is believed to retrieve useful data from enormous raw data. It is applied in several fields in which if there is a big dataset which consists of information about a person’s name, age, address, height, weight, medical test details etc.,. Statistics department may be interested to get the attributes such as name, age and address. Health department may be interested in medical details. They may need to get the ratio of old aged people and wanted to give vaccination to prevent Covid-19. From these raw data, they predict the percentage of Old aged people and the exact number of people who needs more attention. Mere dependence on machine models for benevolence sometimes leads to huge malfunction or loss. So it is advisable to supervise the machine models by having check points. Interaction with the machine is cumbersome and it is fully dependent on the context of the model. In this paper, a different approach is presented for employee work planning schedule. In a moderate firm, employee plan schedule on everyday basis requires a machine model. Genetic Algorithm is used for staff planning and interactive machine learning approach is used for supervision. Human supervision reduces the cost and the algorithm converges rapidly.
Weights Having Stable Signs Are Important: Finding Primary Subnetworks and Kernels to Compress Binary Weight Networks

Binary Weight Networks (BWNs) have significantly lower computational and memory costs compared to their full-precision counterparts. To address the non-differentiable issue of BWNs, existing methods usually use the Straight-Through-Estimator (STE). In the optimization, they learn optimal binary weight outputs represented as a combination of scaling factors and weight signs to approximate 32-bit floating-point weight values, usually with a layer-wise quantization scheme. In this paper, we begin with an empirical study of training BWNs with STE under the settings of using common techniques and tricks. We show that in the context of using batch normalization after convolutional layers, adapting scaling factors with either hand-crafted or learnable methods brings marginal or no accuracy gain to final model, while the change of weight signs is crucial in the training of BWNs. Furthermore, we observe two astonishing training phenomena. Firstly, the training of BWNs demonstrates the process of seeking primary binary sub-networks whose weight signs are determined and fixed at the early training stage, which is akin to recent findings on the lottery ticket hypothesis for efficient learning of sparse neural networks. Secondly, we find binary kernels in the convolutional layers of final models tend to be centered on a limited number of the most frequent binary kernels, showing binary weight networks may has the potential to be further compressed, which breaks the common wisdom that representing each weight with a single bit puts the quantization to the extreme compression. To testify this hypothesis, we additionally propose a binary kernel quantization method, and we call resulting models Quantized Binary-Kernel Networks (QBNs). We hope these new experimental observations would shed new design insights to improve the training and broaden the usages of BWNs.
Key-Nets: Optical Transformation Convolutional Networks for Privacy Preserving Vision Sensors

Modern cameras are not designed with computer vision or machine learning as the target application. There is a need for a new class of vision sensors that are privacy preserving by design, that do not leak private information and collect only the information necessary for a target machine learning task. In this paper, we introduce key-nets, which are convolutional networks paired with a custom vision sensor which applies an optical/analog transform such that the key-net can perform exact encrypted inference on this transformed image, but the image is not interpretable by a human or any other key-net. We provide five sufficient conditions for an optical transformation suitable for a key-net, and show that generalized stochastic matrices (e.g. scale, bias and fractional pixel shuffling) satisfy these conditions. We motivate the key-net by showing that without it there is a utility/privacy tradeoff for a network fine-tuned directly on optically transformed images for face identification and object detection. Finally, we show that a key-net is equivalent to homomorphic encryption using a Hill cipher, with an upper bound on memory and runtime that scales quadratically with a user specified privacy parameter. Therefore, the key-net is the first practical, efficient and privacy preserving vision sensor based on optical homomorphic encryption.
KEEFEKTIFAN MODEL PEMBELAJARAN ROLE PLAYING PADA MATA KULIAH RETORIKA

His study intends to describe the ability of students in rhetoric subjects in the subject of Application of by using a role playing model and to describe the effectiveness of this role playing model. This study uses experimental research methods. Experimental research itself is a study that is intended to determine whether there is a consequence of something imposed on the subject under investigation. Role playing is more effective in learning rhetoric compared to not using role playing models in learning rhetoric in class IV Indonesian Language and Literature Education Study Programs, Pancasakti University, Tegal. This is evidenced by the results of t count 15.26> t table 1.67 in the calculation using the non-independent t test. Rhetoric learning using a role playing model is more effective than those that do not use role playing in its learning. This result is evidenced by the mean value of the experimental class 83.40 which is greater than the control class which is only 77.92. In learning rhetoric using role playing models, students are more active and creative in practice and understand rhetoric, and students' insights are more related to deepening character in rhetoric.
The Effect of Using Suggestopedia Method on Reading Comprehension in Descriptive Text at Tenth Grade of SMA N 8 Kota Jambi

This research was done to find the effect of using suggestopedia method on reading comprehension in descriptive text at tenth grade of SMA N 8 Kota Jambi Academic Year 2019/2020. The researcher used a quantitative research and experimental research design as the research methodology with random sampling. The objective of this research was to find out whether there was significant effect of using sugegstopedia method on reading comprehension in descriptive text at tenth grade of SMA N 8 Kota Jambi. The sample of this research was at tenth grade of SMA N 8 Kota Jambi. The instrument of this research was a reading test. This study involved 60 students, there was consist of 30 students of control class (X MIPA 4) and 30 students of experimental class (X MIPA 2) at tenth grade of SMA N 8 Kota Jambi academic year of 2019/2020 as the object of the study and divided into two classes. Reading comprehension in descriptive text is effect after treatment suggestopedia method which is proven with student’s score higher than before treatment. The average pre-test control class 38,6 and post-test control class 53,3. Average pre-test experimental class 46 and post-test experimental class 90,8. Suggestopedia method makes learning process more interesting. The value of t-test higher than the value of t-table (20,41>2,001) based on the finding above, it can be concluded that suggestopedia method has a significant effect of reading comprehension in descriptive text at tenth grade of SMA N 8 Kota Jambi. Key Words : Suggestopedia, Reading Comprehension, Descriptive Text.
SCSSnet: Learning Spatially-Conditioned Scene Segmentation on LiDAR Point Clouds.

This work proposes a spatially-conditioned neural network to perform semantic segmentation and geometric scene completion in 3D on real-world LiDAR data. Spatially-conditioned scene segmentation (SCSSnet) is a representation suitable to encode properties of large 3D scenes at high resolution. A novel sampling strategy encodes free space information from LiDAR scans explicitly and is both simple and effective. We avoid the need for synthetically generated or volumetric ground truth data and are able to train and evaluate our method on semantically annotated LiDAR scans from the Semantic KITTI dataset. Ultimately, our method is able to predict scene geometry as well as a diverse set of semantic classes over a large spatial extent at arbitrary output resolution instead of a fixed discretization of space. Our experiments confirm that the learned scene representation is versatile and powerful and can be used for multiple downstream tasks. We perform point-wise semantic segmentation, point-of-view depth completion and ground plane segmentation. The semantic segmentation performance of our method surpasses the state of the art by a significant margin of 7% mIoU.
LibGuides: ESB SLS Tips and Tricks: Library Resources

Tips and tricks on a variety of resources found on the Virtual Reference Collection and the Digital Media Library
Incident Location Distance and Transportation to Hospital Delayed Arrival Patients Post-Acute Ischemic Stroke Attack in Emergency Department East Java-Indonesia

Introduction:The main problem faced by patients suffering from acute ischemic stroke for acquiring an immediate treatment is hospital-delayed arrival.Delays are defined as the onset action for stroke starting from the stroke onset to arrival at the ED.The study aimed to investigating the influence of incident location distance and transportation to arrival delay of patients with acute ischemic stroke attack in the ED. Methods:The study was employed cross sectional designed with the total samples were 58 respondents.The samples were relatives of patients suffering from acute ischemic stroke.Observation sheet and interview guide were used as the instruments, while Google Maps application was utilized to measure the distance of the incident locations to the hospital. Results:The results have shown that the average distance between the hospital and the incident location was 3.5 -18.4 kilometres, whereas the delayed rate was from 2.4 to 48.4 hours.Multiple linear regression analysis has shown that the means of transportation significantly influenced the arrival delay in the ED, which was 0.015.The result in line with the fact that around 67.25% patients were using their private transportation means (vehicles) and 32.75% patients were using ambulance to reach the ED. Conclusion:The village ambulance is supposed to be initiated and programmed in respond to hospitaldelayed arrival during emergency conditions as well as to established technology through communication system for emergency referral network for local inhabitant.
Psychological Injury, Cyber Crime and Data Breach Damages

Data breaches concerning individuals and organisations are increasingly common despite the growing emphasis on GDPR (General Data Protection Regulation) and organisational security measures. Despite best intentions and quality control of data, sensitive data can have a variety of negative consequences. When a data breach occurs in an organisation, whether public or private, one compromising episode can adversely affect the best of reputations. Such damage to reputation and brand value as a result of a cyber security breach is an increasingly common occurrence. The digital world of immediate accessibility can result in a company’s identity being adversely affected throughout its customer base, and result in a significant loss of customer trust, whether the ‘customer’ be an individual or another corporate entity. The resulting shut down in operations in order to rectify the data breach very easily results in significant loss of revenue. In the UK, between 40-45% of all businesses identified at least one cyber breach in the past 12 months – it is clearly a priority for companies to learn how to keep data secure or face the consequences, one of which could be litigation.
Archer Library: Streaming Media Access: Streaming Media Databases

This Guide provides the various paths towards accessing streaming video and streaming audio/music for U of Regina Library users.
Non-Inherent Feature Compatible Learning

The need of Feature Compatible Learning (FCL) arises from many large scale retrieval-based applications, where updating the entire library of embedding vectors is expensive. When an upgraded embedding model shows potential, it is desired to transform the benefit of the new model without refreshing the library. While progresses have been made along this new direction, existing approaches for feature compatible learning mostly rely on old training data and classifiers, which are not available in many industry settings. In this work, we introduce an approach for feature compatible learning without inheriting old classifier and training data, i.e., Non-Inherent Feature Compatible Learning. Our approach requires only features extracted by \emph{old} model's backbone and \emph{new} training data, and makes no assumption about the overlap between old and new training data. We propose a unified framework for FCL, and extend it to handle the case where the old model is a black-box. Specifically, we learn a simple pseudo classifier in lieu of the old model, and further enhance it with a random walk algorithm. As a result, the embedding features produced by the new model can be matched with those from the old model without sacrificing performance. Experiments on ImageNet ILSVRC 2012 and Places365 data proved the efficacy of the proposed approach.
Clearing the Path for Truly Semantic Representation Learning

The performance of β-Variational-Autoencoders (β-VAEs) and their variants on learning semantically meaningful, disentangled representations is unparalleled. On the other hand, there are theoretical arguments suggesting impossibility of unsupervised disentanglement. In this work, we show that small perturbations of existing datasets hide the convenient correlation structure that is easily exploited by VAE-based architectures. To demonstrate this, we construct modified versions of the standard datasets on which (i) the generative factors are perfectly preserved; (ii) each image undergoes a transformation barely visible to the human eye; (iii) the leading disentanglement architectures fail to produce disentangled representations. We intend for these datasets to play a role in separating correlation-based models from those that discover the true causal structure. The construction of the modifications is non-trivial and relies on recent progress on mechanistic understanding of β-VAEs and their connection to PCA, while also providing additional insights that might be of stand-alone interest.
Machine Learning for intelligent avatars in Virtual Reality simulations: Analysis of Reinforcement Learning techniques

Recent advances in Deep Reinforcement Learning (RL) allowed driving the avatar in increasingly realistic and complex environments, even Virtual Reality (VR) simulations. The granted wide degrees of freedom and the immersivity of the human in the virtual space, require the interaction with believable Non-Player Characters (NPCs) with non-scripted behaviors and able to adapt to contingent changes. This work focuses on the creation of ML-driven intelligent avatars able to behave an interact with a human in Virtual Reality simulations. Their behavior is decided by a neural network able to perceive the surrounding environment and to perform an action accordingly. This approach allows completing the assigned procedure while reacting to external stimuli, adapting to the human choices, and increasing the variance of the NPC behaviors. In particular are analyzed and compared different RL techniques, such as Curriculum Learning (progressive task complexity) and Imitative Learning (mimicking the proposed human solutions).
Theoretical and Applied Study of the Psychological and Educational Effects of Coronavirus in Primary School Students in Argentina

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Domain engineering as a solution for bridging the gap between ontology engineers and domain experts

One of the main challenges in software systems development is reusability.The interaction between traditional software systems is taking place through their interfaces.Thus, as more software systems are developed, the complexity of the interconnection between the interfaces grows dramatically.This complexity resulted in a decrease in development time and code quality.The role of a subject-matter expert (SME) or Domain Expert, a person with knowledge in a specific area, emerged to tackle this problem and increase the software reusability by modeling the domain rather than the technology.It resulted in a collaboration between the domain experts and developers and sped up the software development life cycle.Similarly, the role of DevOps emerged to bridge the gap between developers and operators in order to automate the process between software development and production release.The main challenge today is to achieve reusability and interconnections between domains of the same or different interests.The ontology concept was introduced as an engineering artifact to describe a reality semantically.Ontologies aim to represent information in a way to be understood and processed by a computer.Therefore, an Ontology Engineer is a person with knowledge for ontology's vocabulary, rules of inference, logic, and its construction.Common domain description languages often lack the semantic representation of their entities.By contrast, the semantic meaning of a domain of interest can be expressed using the ontology's vocabularies and axioms.The inadequate conceptual knowledge representation of a domain expert and the inadequate domain knowledge of an ontology engineer yields a gap between both worlds.Thus, the usage of ontologies in the domain modeling is yet considered to be a challenge.Domain Engineering is an approach that aim for the creation and development of domains on semantic bases.This work focus on investigating the challenges and limitations in the syntactic- and semantic-based development fields.The main objective is to approach the domain engineering as a solution for bridging the gap between domain experts and ontology engineers.Also, the study introduces a domain knowledge development life cycle approach to help by the creation and development of domain representations on semantic bases.The research is conducted in three segments.First by defining the problem and limitation, then conducting extensive literature and related work review to scrutinize the domain experts and ontology engineers roles based on some criteria in their scope of an intersection.After that, investigate existing approaches and tools to construct the domain knowledge life cycle toolchain.Finally, the study concludes the essential presence of both the domain knowledge, presented by the domain expert, and the formalization of semantic conceptualization, presented by the ontology engineer, in the domain engineering approach.