Reviewerly/exHarmony huggingface dataset:
The effect of mobile application programming on the educational process

Distance learning is something really familiar nowadays. In the past few years, our world has witnessed a massive revolu-tion in the world of information technology and provided simple ways to obtain information at reasonable prices. Today, sharing information is an integral part of every person's life. Devices such as phones, tablets, and smartphones have be-come the main focus of digital life. With technological progress, electronic devices go through stages of software develop-ment that allow them to perform additional, more complex tasks, and process a larger flow of information. Almost every-one between the ages of 10 and 60 owns a mobile device. So mobile apps are always up to date. Therefore, it is quite logi-cal to use the mobile application as an auxiliary element in the educational system, especially as it provides many services such as accessing the Internet, browsing the web, sending e-mails, exchanging multimedia messages and files and provid-ing instant messaging applications with audio and video. Focusing on employing technology in education and its curricula, regardless of its level, is considered very important. This work diagnoses what users prefer when using the e-learning appli-cation through a questionnaire distributed to a group of university students to collect data on their preferences regarding mobile applications for e-learning.
Towards Balanced Defect Prediction with Better Information Propagation

Defect prediction, the task of predicting the presence of defects in source code artifacts, has broad application in software development. Defect prediction faces two major challenges, label scarcity, where only a small percentage of code artifacts are labeled, and data imbalance, where the majority of labeled artifacts are non-defective. Moreover, current defect prediction methods ignore the impact of information propagation among code artifacts and this negligence leads to performance degradation. In this paper, we propose DPCAG, a novel model to address the above three issues. We treat code artifacts as nodes in a graph, and learn to propagate influence among neighboring nodes iteratively in an EM framework. DPCAG dynamically adjusts the contributions of each node and selects high-confidence nodes for data augmentation. Experimental results on real-world benchmark datasets show that DPCAG improves performance compare to the state-of-the-art models. In particular, DPCAG achieves substantial performance superiority when measured by Matthews Correlation Coefficient (MCC), a metric that is widely acknowledged to be the most suitable for imbalanced data.
Inter-domain curriculum learning for domain generalization

Domain generalization aims to learn a domain-invariant representation from multiple source domains so that a model can generalize well across unseen target domains. Such models are often trained with examples that are presented randomly from all source domains, which can make the training unstable due to optimization in conflicting gradient directions. Here, we explore inter-domain curriculum learning (IDCL) where source domains are exposed in a meaningful order to gradually provide more complex ones. The experiments show that significant improvements can be achieved in both PACS and Office–Home benchmarks, and ours improves the state-of-the-art method by 1.08%.
Size & Shape Matters: The Need of HPC Benchmarks of High Resolution Image Training for Deep Learning

One of the purposes of HPC benchmarks is to identify limitations and bottlenecks in hardware. This functionality is particularly influential when assessing performance on emerging tasks, the nature and requirements of which may not yet be fully understood. In this setting, a proper benchmark can steer the design of next generation hardware by properly identifying said requirements, and quicken the deployment of novel solutions. With the increasing popularity of deep learning workloads, benchmarks for this family of tasks have been gaining popularity. Particularly for image based tasks, which rely on the most well established family of deep learning models: Convolutional Neural Networks. Significantly, most benchmarks for CNN use low-resolution and fixed-shape (LR&amp;FS) images. While this sort of inputs have been very successful for certain purposes, they are insufficient for some domains of special interest (e.g., medical image diagnosis or autonomous driving) where one requires higher resolutions and variable-shape (HR&amp;VS) images to avoid loss of information and deformation. As of today, it is still unclear how does image resolution and shape variability affect the nature of the problem from a computational perspective. In this paper we assess the differences between training with LR&amp;FS and HR&amp;VS, as means to justify the importance of building benchmarks specific for the latter. Our results on three different HPC clusters show significant variations in time, resources and memory management, highlighting the differences between LR&amp;FS and HR&amp;VS image deep learning.
Low Probability of Intercept (LPI) Radar Signal Identification Techniques

A structure which a makes use of both Radar and Electronic Support tools is known as Low Probability of Intercept.Evading identification by ES receiver is the main aim behind the LPI radar.Features like frequency variability, low power, and wide bandwidth lend the property of Low probability of Intercept to the emitter making it undetectable by passive intercept devices like electronic intelligence receivers, radar warning, and electronic support.Novel signal processing methods are needed for identifying LPI radar waveforms.To retrieve meaningful information from the fed signal, Higher Order Spectral Analysis algorithms are utilized.Bispectrum, Bicoherence and Trispectrum techniques comprise the methodologies.For every LPI signal, the images generated by the algorithms are distinct and mean to be identifiers.
Interventionism and Non-Causal Dependence Relations: New Work for a Theory of Supervenience

Causes must be distinct from their effects. If the temperature in a room is 15°F, this can cause water pipes to freeze. However, the temperature’s being 15°F is not a cause of the temperature’s being below the freezing point. In general, conceptual, logical, mathematical, and other non-causal dependence relations should not be misclassified as causal. In this paper, I discuss how interventionist theories of causation can meet the challenge of distinguishing between (direct or indirect) causal relations and dependence relations that are due to non-causal factors. I survey several approaches to solving this problem, and propose a novel criterion based on the notion of supervenience. According to this criterion, variables are sufficiently distinct to qualify as the relata of a causal relation iff they do not have overlapping minimal supervenience bases.
An Efficient Implementation of Hybrid Carry Select Adder Using Parallel Prefix Addition and Binary to Excess 1 Conversion

One of the fastest adder architecture among the prevailing adder architectures. Adders are digital logic devices that add binary numbers together. They are commonly used as a component in an Arithmetic Logic Unit which itself is a component in Central Processing Unit. As a result any electronic device that has a Microcontroller or a CPU such as smart thermostats, digital alarm clock, digital wrist watches, and digital bathroom scales etc use adder circuit. In this work, we present an 8 bit hybrid Carry Select Adder architecture. It employs Parallel prefix addition using Kogge stone adder structure, Brent Kung adder structure, Han Carlson Adder structure and Ladner Fischer adder structures. It also employs Binary to Excess 1 code converter along with parallel prefix adders. Affirmation of the suggested design completed using Verilog code and simulated using Xilinx ISE 14.7 and the power, area and delay results were calculated using Cadence software. Comparisons with existing conventional adder architecture certify its better quality. The experimental analysis probes that the recommended hybrid carry select adder results threefold advantages in regards with speed, area and power.
Synergetic mechanisms of students’ new worldview formation

The authors consider the synergetic approach in psycho-pedagogics. Synergeticsis regarded nowadays as one of the fundamental aspects of the students’ new worldview formation. Teachers’ professional awareness to work in the framework of new tendencies is one more urgent task. Creative and prognostic thinking will help teachers to work with students as self-organizing systems. The proper new original technique and style elaboration will greately contribute to educational process. Such Synergetic mechanisms as openness, nonlinearity; feedback are the leading ones in creating modern terms of teaching and upbringing students, defining them as a super-complexes and unique systems. Synergetics can be of great assistance in creating modern terms of teaching and upbringing students. The synergetic approach is a scientific domain of evolving structurally organized systems, to which the system of professional training of specialists can be referred. This approach creates new possibilities to student’s self-determination in the framework of multidimensionality, complexity, and alternativeness of cognizable processes.It is of great importance to recognize the significant role of randomness in students’ self-development. It is necessary to emphasize that systems including several complex system components that make up an integral synergetic system are actually realized in the conditions of diversity, randomness, spontaneity and unpredictability. The problem is that a future specialist should have the skills and professional mobility to react quickly to the constantly emerging changes in practical and scientific activity. Thus, interdisciplinary integration is one of the most important areas for improving the preparation of students in a modern higher educational institution.
Zeph: Cryptographic Enforcement of End-to-End Data Privacy

As increasingly more sensitive data is being collected to gain valuable insights, the need to natively integrate privacy controls in data analytics frameworks is growing in importance. Today, privacy controls are enforced by data curators with full access to data in the clear. However, a plethora of recent data breaches show that even widely trusted service providers can be compromised. Additionally, there is no assurance that data processing and handling comply with the claimed privacy policies. This motivates the need for a new approach to data privacy that can provide strong assurance and control to users. This paper presents Zeph, a system that enables users to set privacy preferences on how their data can be shared and processed. Zeph enforces privacy policies cryptographically and ensures that data available to third-party applications complies with users' privacy policies. Zeph executes privacy-adhering data transformations in real-time and scales to thousands of data sources, allowing it to support large-scale low-latency data stream analytics. We introduce a hybrid cryptographic protocol for privacy-adhering transformations of encrypted data. We develop a prototype of Zeph on Apache Kafka to demonstrate that Zeph can perform large-scale privacy transformations with low overhead.
Adaptive grid‐driven probability hypothesis density filter for multi‐target tracking

The probability hypothesis density (PHD) filter and its cardinalised version PHD (CPHD) have been demonstratedasa class of promising algorithms for multi-target tracking (MTT) with unknown,time-varying number of targets. However, these methods can only be used in MTT systems with some prior information of multipletargets, such asdynamic model, newborn target distribution etc.;otherwise, the tracking performance will decline greatly. To solve this problem,an adaptive Grid-driven technique is proposed based on the framework of the PHD/CPHD filter to recursively estimate the target states without knowing the dynamic model and the newborn target distribution. The grid size can be adaptively adjusted according to the grid resolution, and the dynamic tendencies of the grids can respond to the unknown dynamic models of each targets, including arbitrary manoeuvring models. The newborn targets outside the grid area can be identified by analysing the measurements, and some new grids are generated around them. The experimental results show that the proposed algorithm has a better performance than the traditional particle filter-based PHD method in terms of average optimal sub-pattern assignment distance and average target number estimation for tracking multiple targets with unknown dynamic parameters and unknown newborn target distribution.
Peculiarities Of High School Student's Tolerance Towards Creative Person

The value of the human capital as well as sociocultural specificities considered by the Russian venture company and Institute of national projects to be an important factors of technological development of the society and individual innovation activity.Despite existing demand for people creative, tolerant to uncertainty and able to be productive in the hard and unstable VUCA-world, there are problematic tendency in crisis to reject creative potential in favor of more pragmatic or hedonistic lifestyle.In the context of development of creative life path, it's important to study high school student's attitude towards creative person.The purpose of this research is to identify peculiarities of the role identification and tolerance towards different types of creative people among high school students.The sample consisted of 66 high school students from Chelyabinsk city.For the research purpose we used «Role Relations between Social Subjects and Creative Personalities» (RRSSCP) methodology, developed by Gryazeva-Dobshinskaya V.G., Bakunchik N.Y., Glukhova V.A., Maltseva A.S., based on Kelly's Repertory Grid Technique.Results calculated through method of factor analysis.Following peculiarities of high school student's tolerance towards creative person were identified: high level of tolerance towards the role of «Creator» in organizational structure and towards those creative people, who either show leadership potential or already hold high-status positions; identification of own self with creative persons with low levels of intolerance.The analyzed data shows necessity of development of vocational guidance programs and trainings, focused on development of attractive images of creative person with differential life strategies.
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning.

Agents that interact with other agents often do not know a priori what the other agents' strategies are, but have to maximise their own online return while interacting with and learning about others. The optimal adaptive behaviour under uncertainty over the other agents' strategies w.r.t. some prior can in principle be computed using the Interactive Bayesian Reinforcement Learning framework. Unfortunately, doing so is intractable in most settings, and existing approximation methods are restricted to small tasks. To overcome this, we propose to meta-learn approximate belief inference and Bayes-optimal behaviour for a given prior. To model beliefs over other agents, we combine sequential and hierarchical Variational Auto-Encoders, and meta-train this inference model alongside the policy. We show empirically that our approach outperforms existing methods that use a model-free approach, sample from the approximate posterior, maintain memory-free models of others, or do not fully utilise the known structure of the environment.
SLA amongst Users and Providers in Multi-Cloud Environment Through Negotiation Model

&lt;p class="0abstract"&gt;Cloud Computing conducts application, infrastructure services or platform to a very large amount of users with more choices and continuous changing requirements. Cloud providers are occupied in organizing data warehouses to arrange the continuous growth in cloud user’s acceptance. Features of cloud computing services have afforded an important tendency of companies choosing these services. In this case, many cloud users, who intend a certain service, and many cloud providers, who provision those services, create a competitive market. When constructing a Cloud scheduling scheme, the project trade-offs of the Cloud architecture should be rated. The simplest manner to rate this infrastructure is to use a simulation tool called CloudSim simulation toolkit. To reduce/avoid SLA violations in the cloud computing system, in our work we propose a SLA reduction framework in which we have considered three steps: Migration control of VMs, Energy efficiency and VmScheduling. It is also implemented in Cloudsimulator.&lt;/p&gt;
Computing Opportunities to Augment Plans for Novel Replanning during Execution

Traditionally, planning provides for execution plans as sequences of actions with preconditions and effects. Execution monitoring identifies failure conditions when the preconditions of an action do not match the state. Interestingly, planning proceeds by consuming a given initial state and abandoning reasoning about any facts not true in that state. In this paper, we define opportunities as such missing facts, and contribute an algorithm to compute them and augment a plan for execution with them. We then introduce a new execution opportunity monitoring that focusedly checks for these opportunities at each execution state. Opportunistic replanning proceeds now from the new state including the detected opportunities.
Metaconvergence as a methodological strategy of education in information culture

The aim of the study is to formulate a methodological education strategy that is adequate to the modern socio-cultural situation, which will allow, when using the achievements of digital technologies, to maintain the humanistic dimension of the student’s consciousness. The scientific novelty of the research lies in the proposed methodological educational strategy – meta convergence. Metaconvergence is a way of realizing an educational co-being that initiates the reproduction of cultural matrices in the “sphere of one’s own” subject of education. Metaconvergence, forming the ontological prerequisites of knowledge, makes it possible to organize pedagogical communication in a special way, which is capable of synthesizing educational, personal and cultural dimensions into a single tissue. The research methods were phenomenological and systemic approaches. The phenomenological method made it possible to structure the problem of the methodology of education in the context of the premiselessness, as well as to thematize education in terms of the ability to form meaning within the boundaries of the subject knowledge. The use of the procedures of the systemic method made it possible to build the realizable tasks of the educational process into a system, the core of which is the concept of metaconvergence as a methodological educational strategy. As a result the use of the declared methodological educational strategy, educational tasks of the humanitarian, communicative, motivational and axiological spectrum are solved. It is shown that meta convergence makes it possible to overcome the boundaries of disciplinary paradigms and to approve the need for a humanitarian attitude at an interdisciplinary level.
Inaudible Manipulation of Voice-Enabled Devices Through BackDoor Using Robust Adversarial Audio Attacks

The BackDoor system provides a method for inaudibly transmitting messages that are recorded by unmodified receiver microphones as if they were transmitted audibly. Adversarial Audio attacks allow for an audio sample to sound like one message but be transcribed by a speech processing neural network as a different message. This study investigates the potential applications of Adversarial Audio through the BackDoor system to manipulate voice-enabled devices, or VEDs, without detection by humans or other nearby microphones. We discreetly transmit voice commands by applying robust, noise-resistant adversarial audio perturbations through BackDoor on top of a predetermined speech or music base sample to achieve a desired target transcription. Our analysis compares differing base carriers, target phrases, and perturbation strengths for maximal effectiveness through BackDoor. We determined that such an attack is feasible and that the desired adversarial properties of the audio sample are maintained even when transmitted through BackDoor.
Mobile-Assisted Language Learning Based Using Scientific Approach to Improve Students Vocabulary

One of the hard sections of learning the target language for English language learners is vocabulary. Vocabulary plays a significant part in language learning because without vocabulary the students cannot master 4 skills (speaking, listening, reading, and writing). So many problems were found in learning vocabulary which influence students’ vocabularies mastery and to solve it the researcher addresses the alternative strategy which can be used for teaching learning vocabulary. Based on previous studiess technology gave the positive effect on learning English. Thus, making different with the previous studies, the researcher applied MALL based using scientific Approach to teaching vocabulary. The objective of the study is to find out the significant effect of MALL based using scientific Approach to improve students’ vocabulary. The design of this study was quasi experiment design. The researcher chose eight grade students of SMPN 4 Babat which totally consisted of 48 students. The researcher chose the sample of the study through population sampling. The sample was divided 24 students of VIII-A as experiment group and 24 students of VIII-B as control group. The data was collected by using pre-test and post-test about active and passive vocabulary. After getting the data, the researcher analyzed the data using SPSS 16.00 and independent Sample T-test. The research finding showed that there was significant difference between experimental group and control group. It has been proven by the result of sig. (2-Tiled) is .002. the sig (2-Tiled) is lower 0.05 (0.001 &lt; 0.05). So, null hypothesis (Ho) can be rejected. It means that MALL based using scientific approach significantly affects students vocabulary. The researcher concluded that MALL based using scientific approach give positive effect on students’ vocabulary ability. The researcher suggests the English teacher to implement MALL based using scientific approach as an alternative strategy in teaching learning process. For the next researcher, they can implement MALL for higher level students’ using other media.
Implementation of language development for children in daycare

Language development activities in daycare are essential because they can increase children's vocabulary. This study aims to determine how the process of implementing children's language development activities in daycare centres. This study uses the qualitative method. The data were collected by observing the language development of children aged 2-4 years at the Twin Course Daycare and interviewing six caregivers there. The study results showed that the implementation of language development in the Twin Course daycare centre was carried out well. It can be seen from how the teacher applies it in daily activities by reading stories, playing games that can stimulate children's language, listening to the radio, dancing, and responding to children. Therefore, research related to the effective implementation of the language development program can be studied further.
Wavelet Transform based Steganography

Steganography is a technique which is used to hide audio signal behind an image in the transform domain using wavelet transform. The audio signal can be of any type, any format like MP3 or WAV or any other type is encrypted and hide behind an image without disclosing even the existence of audio signal to anyone. It gives the best way to send an audio signal by hiding the secret data.