Reviewerly/exHarmony huggingface dataset:
Model Desain Pembelajaran Tematik Terpadu Kontekstual Untuk Meningkatkan Kebermaknaan Belajar Siswa SD

This study aims to: 1) develop syntax of instructional contextual integrated thematic design models to improve the meaningful learning of elementary students; 2) describe an validity models; and 3) describe the level of effectiveness of the model. This R&amp;D research consists of a preliminary study, model designing and developing to be validated by experts, the model testing. The preliminary field testing involved 4 teachers and 57 elementary students participants. The main field testing involved 8 teachers and 94 students. The research instrument are questionnaires and test. This research findings are: 1) the model steps includes: a) setting a theme; b) adding sub-themes; c) the analysis of SKL, KI, KD and making indicators; d) the making of nets of sub-themes; e) developing syllabus; f) developing RPP; g) compiling a fragment of the teacher's book; and h) developing fragments of student books; 2) the model validity level is in the very high category (86%), the syllabus and lesson plans and the material are in the high category (69.8%; 69%); and 3) the meaningfulness of student learning has increased, namely in the preliminary field test increased from 49.49 in pretest to 69.44 in post-test, and in the broad field test, the value increased greater than 47.98 to 70.43. The statistical test using the t-test ensures that the increase is significant, because the probability value (0.00) is smaller than the value of α = 0.05.
Artificial Bee Colony Algorithm Combined With Previous Successful Search Experience

The neighboring solutions-based technique employed by the artificial bee colony algorithm (ABC) is good at exploration but poor at exploitation. The main reason is the blindness of search behavior which leads to the employed bees not generating promising candidate solutions. To address this issue, we propose an improved ABC algorithm (ABCPSE) combined with the previous successful search experience. The proposed algorithm has the following innovative advantages: First, the previous successful search experience with good performances in convergence and distribution are employed in real time to generate offspring. This rule can increase convergence speed and exploitation ability of ABCPSE algorithm. Second, local search is performed near the superior individuals produced in a different generation. Hence, a set of solutions with excellent diversity and convergence is obtained. To assess the performance of ABCPSE algorithm, experiments are conducted on a set of 18 benchmark functions. The results demonstrate that the proposed algorithm can produce higher quality solutions with faster convergence than some current state-of-the-art ABC-based algorithms.
Displaying data is not enough: Incorporating User Behavior Transformation in domestic reporting systems

Studies have shown that aspects relating to user behavior play a decisive role in energy consumption reduction, namely in the residential sector, it is expected that domestic reporting systems will elicit user support in identifying sources of inefficient energy usage, leading them to identify and implement both preventive and corrective actions. However, these systems have shortcomings in features encouraging User Behavior Transformation (UBT); how to design domestic reporting user interfaces in a way that effectively triggers UBT still remains an open issue. This article discusses relevant aspects of UBT and analyzes related work in the area, identifying a set of design principles to be applied when developing user interfaces towards residential energy consumers. A thorough evaluation of UBT-enabling design dimensions of current solutions for the domestic sector is undertaken and our conclusions reinforce that these systems still fall behind in addressing UBT, namely when considering social dimensions.
On Many-to-Many Mapping Between Concordance Correlation Coefficient and Mean Square Error.

The concordance correlation coefficient (CCC) is one of the most widely usedreproducibility indices, introduced by Lin in 1989. In addition to itsextensive use in assay validation, CCC serves various different purposes inother multivariate population-related tasks. For example, it is often used as ametric to quantify an inter-rater agreement. It is also often used as aperformance metric for prediction problems. In terms of the cost function,however, there has been hardly any attempt to design one to train thepredictive deep learning models. In this paper, we present a family oflightweight cost functions that aim to also maximise CCC, when minimising theprediction errors. To this end, we first reformulate CCC in terms of the errorsin the prediction; and then as a logical next step, in terms of the sequence ofthe fixed set of errors. To elucidate our motivation and the results we obtainthrough these error rearrangements, the data we use is the set of gold standardannotations from a well-known database called `Automatic Sentiment Analysis inthe Wild' (SEWA), popular thanks to its use in the latest Audio/Visual EmotionChallenges (\textsc{AVEC'17} and \textsc{AVEC'18}). We also present some newand interesting mathematical paradoxes we have discovered through this CCCreformulation endeavour.
INTEGRATING DIGITAL TECHNOLOGIES INTO TEACHING PROCESS

This paper wishes to highlight great significance of digital technologies that should be implemented to the highest extent possible in the teaching process. Digital literacy and digital competencies in today's highly developed technological world have been recognized as important aspects for the successful implementation of digital technologies in the teaching process. With this in mind, in this paper we tried to answer the questions as to for what purposes teachers use digital technologies the most and how interested the teachers are for the use of digital technologies. The conclusion is that teachers are highly interested in the application of digital technologies in teaching. Out of all the listed items, the teachers mostly used the Internet-based content and Correspondence by e-mail in their work. The research was based on a descriptive investigative method. Surveys and scaling techniques for measuring teachers' attitudes were used. The present research was carried out between March and April 2017.The basic cohort in this research comprised 276 primary school teachers in the cities of Vranje and Nis. The nature of a sample is that of a stratified simple random sample.
Time analysis of actor programs

This paper proposes a technique for estimating the computational time of programs in an actor model, which is intended to serve as a compiler target of a wide variety of actor-based programming languages. We define a compositional translation function returning cost equations, which are fed to an automatic off-the-shelf solver for obtaining the time bounds. Our approach is based on a new notion of synchronization sets, which captures possible difficult synchronization patterns between actors and helps make the analysis efficient and precise. The approach is proven to correctly over-approximate the worst computational time of an actor model of concurrent programs. Our technique is complemented by a prototype analyzer that returns upper bound of costs for the actor model.
Understanding the Dynamics of Social Interactions

In this article, we deal with the problem of understanding human-to-human interactions as a fundamental component of social events analysis. Inspired by the recent success of multi-modal visual data in many recognition tasks, we propose a novel approach to model dyadic interaction by means of features extracted from synchronized 3D skeleton coordinates, depth, and Red Green Blue (RGB) sequences. From skeleton data, we extract new view-invariant proxemic features, named Unified Proxemic Descriptor (UProD), which is able to incorporate intrinsic and extrinsic distances between two interacting subjects. A novel key frame selection method is introduced to identify salient instants of the interaction sequence based on the joints’ energy. From Red Green Blue Depth (RGBD) videos, more holistic CNN features are extracted by applying an adaptive pre-trained Convolutional Neural Networks (CNNs) on optical flow frames. For better understanding the dynamics of interactions, we expand the boundaries of dyadic interactions analysis by proposing a fundamentally new modeling for non-treated problem aiming to discern the active from the passive interactor. Extensive experiments have been carried out on four multi-modal and multi-view interactions datasets. The experimental results demonstrate the superiority of our proposed techniques against the state-of-the-art approaches.
Exploring Deep-Based Approaches for Semantic Segmentation of Mammographic Images

Pectoral muscle and background elimination are common steps for automated software in mammographic image preprocessing. We investigate FCNs, U-nets and SegNets in the task of mammogram segmentation, addressing three subtasks: pectoral muscle, background and breast region segmentation. The MIAS and INbreast datasets were used for evaluating Deep Neural Networks on the segmentation of these regions. Several objective evaluation metrics were used in order to compare our results with the ones available in the literature. State-of-the-art results were observed in most comparisons, significantly surpassing the baselines in most metrics. Best Jaccard values (in %) for Deep Learning algorithms were $$89.7\pm 2.5$$ , $$98.4\pm 0.1$$ and $$97.0\pm 0.4$$ for pectoral muscle, background and breast region segmentation, respectively, in the MIAS dataset. For INbreast, the best Jaccard value achieved for pectoral muscle segmentation was $$90.8\pm 2.5$$ .
Meta-Learning Neural Bloom Filters

There has been a recent trend in training neural networks to replace data structures that have been crafted by hand, with an aim for faster execution, better accuracy, or greater compression. In this setting, a neural data structure is instantiated by training a network over many epochs of its inputs until convergence. In applications where inputs arrive at high throughput, or are ephemeral, training a network from scratch is not practical. This motivates the need for few-shot neural data structures. In this paper we explore the learning of approximate set membership over a set of data in one-shot via meta-learning. We propose a novel memory architecture, the Neural Bloom Filter, which is able to achieve significant compression gains over classical Bloom Filters and existing memory-augmented neural networks.
A serendipity-biased Deepwalk for collaborators recommendation

Scientific collaboration has become a common behaviour in academia. Various recommendation strategies have been designed to provide relevant collaborators for the target scholars. However, scholars are no longer satisfied with the acquainted collaborator recommendations, which may narrow their horizons. Serendipity in the recommender system has attracted increasing attention from researchers in recent years. Serendipity traditionally denotes the faculty of making surprising discoveries. The unexpected and valuable scientific discoveries in science such as X-rays and penicillin may be attributed to serendipity. In this paper, we design a novel recommender system to provide serendipitous scientific collaborators, which learns the serendipity-biased vector representation of each node in the co-author network. We first introduce the definition of serendipitous collaborators from three components of serendipity: relevance, unexpectedness, and value, respectively. Then we improve the transition probability of random walk in DeepWalk, and propose a serendipity-biased DeepWalk, called Seren2vec. The walker jumps to the next neighbor node with the proportional probability of edge weight in the co-author network. Meanwhile, the edge weight is determined by the three indices in definition. Finally, Top-N serendipitous collaborators are generated based on the cosine similarity between scholar vectors. We conducted extensive experiments on the DBLP data set to validate our recommendation performance, and the evaluations from serendipity-based metrics show that Seren2vec outperforms other baseline methods without much loss of recommendation accuracy.
Dataset on powered two wheelers fall and critical events detection

In this data article, we will present the data coming from 3D Inertial Measurement Unit (3-accelerometers and 3-gyroscopes sensors) mounted on the motorcycle collected during a motorcycle's falls experiments. Developing a motorcycle's fall events detection algorithms is a very challenging task because the motorcycle falling is multi-factorial and is strongly influenced by many unknown factors. To solve this issue, one solution can be to use a data-set collected during controlled experiments, knowing that the real motorcycle falls cannot be replicated, a stuntman can be chosen to be as close to reality as possible. The experiments have been conducted based on predefined scenarios such as: fall in a curve, fall on a slippery straight road section, fall with leaning of the motorcycle ''intentional manoeuvre'' and fall in a roundabout. These scenarios have been designed based on realistic falls. Other experiments have been conducted under different extreme driving situations. These extreme manoeuvres were carried out on track by professional riders. The purpose of performing these manoeuvres was to obtain a dataset describing the limit handling behaviour.
Architecture of Software-Hardware Complex for Searching Images in Database

The article discusses a method to increase the efficiency of solving the problem of finding images in a database. This method is based on the use of perceptual hashing of an image, three levels of data parallelization and image search procedures. To implement parallel data processing, the principle of symmetric horizontal data distribution and the capabilities of modern processors (SIMD registers and corresponding instructions) are used. The results of a computational experiment, confirming the effectiveness of the proposed method, are presented.
Study on the Extent of the Impact of Data Set Type on the Performance of ANFIS for Controlling the Speed of DC Motor

This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) for tracking SEDC motor speed in order to optimize the parameters of the transient speed response by finding out the perfect training data provider for the ANFIS. The controller was adjusted using PI, PD and PIPD to generate data sets to configure the ANFIS rules. The performance of the ANFIS controllers using these the different data sets was investigated. The efficiencies of the three controllers were compared to each other, where the PI, PD, and PIPD configurations were replaced by ANFIS to enhance the dynamic action of the controller. The performance of the proposed configurations was tested under different operating situations. Matlab's Simulink toolbox was used to implement the designed controllers. The resultant responses proved that the ANFIS based on the PIPD dataset performed better than the ANFIS based on the PI and PD data sets. Moreover, the suggested controller showed a rapid dynamic response and delivered better performance under various operating conditions.
A strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changes

In recent decades, methods to find invariant points in digital images, called fiducial points, have gained great attention, mainly due to the demands of several applications in computer vision and image processing, such as the geometric matching of global structures, objects or specific regions. Among the most well known approaches are algorithms like SIFT, HOG, SURF, and their variations as A-SIFT, PCA-SIFT, surrounded by many others. Though the number of researches demonstrating the efficiency of such methods is undoubtedly enormous, the vast majority compares their performances only on pairs of images with little changes in view perspectives, objects or specific regions of the scenes. Thereby, the study of this type of technique under large viewpoint changes, called here LVC, has received little attention from the researchers. On the other hand, with the aim of filtering points of interest, most techniques have used the traditional extensive statistics. However, methods for image processing based on a new type of statistics, called non-extensive statistics, have shown to be efficient in several applications. In this paper, we present a new method, called q-SIFT, based on the non-extensive Tsallis statistics, to find fiducial points in a sequence of frames of videos under large viewpoints changes. We experimentally show the efficiency of the proposed method in video databases and propose new measurement metrics for this type of algorithm.
SuperTML: Two-Dimensional Word Embedding and Transfer Learning Using ImageNet Pretrained CNN Models for the Classifications on Tabular Data

Tabular data is the most commonly used form of data in industry. Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data. DNN models using categorical embeddings are also applied in this task, but all attempts thus far have used one-dimensional embeddings. The recent work of Super Characters method using two-dimensional word embeddings achieved the state of art result in text classification tasks, showcasing the promise of this new approach. In this paper, we propose the SuperTML method, which borrows the idea of Super Characters method and two-dimensional embeddings to address the problem of classification on tabular data. For each input of tabular data, the features are first projected into two-dimensional embeddings like an image, and then this image is fed into fine-tuned two-dimensional CNN models for classification. Experimental results have shown that the proposed SuperTML method had achieved state-of-the-art results on both large and small datasets.
Transfer Feature Generating Networks With Semantic Classes Structure for Zero-Shot Learning

Feature generating networks face a very important issue, which is the fitting difference (inconsistency) of the distribution between the generated feature and the real data. This inconsistency further influences the performance of the network model because training samples from seen classes are disjointed with testing samples from unseen classes in zero-shot learning (ZSL). In generalized zero-shot learning (GZSL), testing samples are from not only seen classes but also unseen classes to be closer to the practical situation. Therefore, most feature generating networks have difficulty achieving satisfactory performance for challenging GZSLs by adversarial learning the distribution of semantic classes. To alleviate the negative influence of this inconsistency for ZSL and GZSL, transfer feature generating networks with semantic classes structure (TFGNSCS) are proposed for constructing a network model to improve the performance of ZSL and GZSL. TFGNSCS not only can consider the semantic structure relationship between seen and unseen classes, but also can learn the difference of generating features by transferring classification model information from seen to unseen classes in networks. The proposed method can integrate the transfer loss, the classification loss and the Wasserstein distance loss to generate enough CNN features, on which softmax classifiers are trained for ZSL and GZSL. Experiments demonstrate that TFGNSCS outperforms state-of-the-art models on four challenging datasets: CUB, FLO, SUN, and AwA in GZSL.
Shaping the Glitch: Optimizing Voltage Fault Injection Attacks

Voltage fault injection is a powerful active side channel attack that modifies the execution-flow of a device by creating disturbances on the power supply line. The attack typically aims at skipping security checks or generating side-channels that gradually leak sensitive data, including the firmware code. In this paper we propose a new voltage fault injection technique that generates fully arbitrary voltage glitch waveforms using off-the-shelf and low cost equipment. To show the effectiveness of our setup, we present new, unpublished firmware extraction attacks on six microcontrollers from three major manufacturers: STMicroelectronics, Texas Instruments and Renesas Electronics that, in 2016 declared a market of $1.5 billion, $800 million and $2.5 billion on units sold, respectively. Among the presented attacks, the most challenging ones exploit multiple vulnerabilities and inject over one million glitches, heavily leveraging on the performance and repeatability of the new proposed technique. We perform a thorough evaluation of arbitrary glitch waveforms by comparing the attack performance against two other major V-FI techniques in the literature. Along a responsible disclosure policy, all the vulnerabilities have been timely reported to the manufacturers.
Apprentice of Oz: Human in the Loop System for Conversational Robot Wizard of Oz

Conversational robots that exhibit human-level abilities in physical and verbal conversation are widely used in human-robot interaction studies, along with the Wizard of Oz protocol. However, even with the protocol, manipulating the robot to move and talk is cognitively demanding. A preliminary study with a humanoid was conducted to observe difficulties wizards experienced in each of four subtasks: attention, decision, execution, and reflection. Apprentice of Oz is a human-in-the-loop Wizard of Oz system designed to reduce the wizard's cognitive load in each subtask. Each task is co-performed by the wizard and the system. This paper describes the system design from the view of each subtask.
Reinforcement Learning for Extended Reality: Designing Self-Play Scenarios

A common problem for deep reinforcement learning networks is a lack of training data to learn specific tasks through generalization.In this paper, we discuss using extended reality to train reinforcement learning agents to overcome this problem.We review popular reinforcement learning and extended reality techniques and then synthesize the information, this allowed us to develop our proposed design for a self learning agent.Meta learning offers an important way forward, but the agents ability to perform self-play is considered crucial for achieving successful AI.Therefore, we focus on improving self-play scenarios for teaching self-learning agents, by providing a supportive environment for improved agent-environment interaction.
A Novel Memcapacitor Model and Its Application for Image Encryption Algorithm

In this paper, a novel chaotic circuit with one memcapacitor is designed, which has abundant dynamic behaviors. As a potential application, combining the DNA binding mode and the characteristics of the chaotic system, a new image encryption scheme is proposed. Firstly, the original image is processed and generates a set of hash values by the SHA-3 algorithm. The generated hash value and original image are performed XOR operation. The hash values are processed to generate the initial value of the chaotic system by the Hamming distance. Furthermore, the elliptic curve with the hyperchaotic sequence is used to construct the Hill encryption matrix, and the plaintext image is permuted and encrypted. At last, the proposed chaotic system is used to scramble the image. The simulation results and theoretical analysis show that the chaotic system has abundant dynamic behaviors, and the proposed algorithm has a better anti-interference ability for image and information encryption.