Reviewerly/exHarmony huggingface dataset:
Highly Protective Framework for Medical Identity Theft by Combining Data Hiding with Cryptography

Medical identity theft is more common since it is easy to steal the patient’s record details and sell it for illegal medical trades. It happens in medical track records, personal healthcare accounts, recorded online insurance services, social security numbers, and other personally identifiable information (PII) which are disguised and used in the name of others. In this research, the proposed approach combines data hiding with hybrid encryption algorithms such as Modified Elliptic Curve Cryptography (MECC) encryption based on MIMO-OFDM framework. The hybrid encryption technique is combined with an LDPC coder for efficient information between the sender and receiver. Thereby, it improves the hiding capacity by 11.36% than SISO, 48.48% than OFDM, and 68.96% than MIMO. The sensitivity values are measured through the parameter number of pixels changes rate (NPCR) and unified average changing ıntensity (UACI) and obtained as 99.70 and 33.48 respectively. Correlation between adjacent pixels (CP) is increased compared to previous works. For SNR (Signal to Noise Ratio) at 30 dB CP is obtained as 0.9985. Simulation is carried out in MATLAB 2019a.
Differentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation

Multi-person pose estimation is challenging because it localizes body keypoints for multiple persons simultaneously. Previous methods can be divided into two streams, i.e. top-down and bottom-up methods. The top-down methods localize keypoints after human detection, while the bottom-up methods localize keypoints directly and then cluster/group them for different persons, which are generally more efficient than top-down methods. However, in existing bottom-up methods, the keypoint grouping is usually solved independently from keypoint detection, making them not end-to-end trainable and have sub-optimal performance. In this paper, we investigate a new perspective of human part grouping and reformulate it as a graph clustering task. Especially, we propose a novel differentiable Hierarchical Graph Grouping (HGG) method to learn the graph grouping in bottom-up multi-person pose estimation task. Moreover, HGG is easily embedded into main-stream bottom-up methods. It takes human keypoint candidates as graph nodes and clusters keypoints in a multi-layer graph neural network model. The modules of HGG can be trained end-to-end with the keypoint detection network and is able to supervise the grouping process in a hierarchical manner. To improve the discrimination of the clustering, we add a set of edge discriminators and macro-node discriminators. Extensive experiments on both COCO and OCHuman datasets demonstrate that the proposed method improves the performance of bottom-up pose estimation methods.
PENGARUH POLA ASUH ORANG TUA DAN MOTIVASI TERHADAP PRESTASI BELAJAR SISWA KELAS XI IPS SMA NEGERI 5 PALU

This study aimed to obtain information about (1) Is there any influence parenting parents on student achievement class XI IPS SMAN 5 Palu. (2) Is there any influence of motivation on student achievement class XI IPS SMAN 5 Palu. The method used is descriptive quantitative method. The population of as many as 133 students at SMAN 5 Palu. Total sample as many as 57 people using Proportional sampling techniques. Data collection techniques using a questionnaire Likert scale models, observation, interview and documentation. The data analysis technique that is the first data reduction, data presentation, and draw conclusions. The results showed that: First, there is the influence of Parenting Students (X1) to the learning achievement (Y), parenting applied in SMA Negeri 5 Hammers Class XI IPS namely Democratic Parenting With the percentage rate of 35,73%. In this case of course the child's success in school with high academic achievement can not be separated from parenting parents, the family is the first social group where children can interact. Family influence in the formation and development of personality is very large. Second, From the analysis it is known that the implications of the results of motivation on learning achievement has significant impact on the increase in student motivation. Motivation of students at SMAN 5 Hammers Class XI IPS can already be said to be good with a percentage of 63.44%. Motivation resulting in students not be separated from the teachers themselves. A student learning because it is driven by the power of mental form of desires, concerns, wishes, or ideals, mental strength and can be classified as low and high depending on the efforts of teachers to encourage students and reward so that students feel able to be better.
Interactive Evaluation of Conversational Agents: Reflections on the Impact of Search Task Design

Undertaking an interactive evaluation of goal-oriented conversational agents (CAs) is challenging, it requires the search task to be realistic and relatable while accounting for the users cognitive limitations. In the current paper we discuss findings of two Wizard of Oz studies and provide our reflections regarding the impact of different interactive search task designs on participants? performance, satisfaction and cognitive workload. In the first study, we tasked participants with finding a cheapest flight that met a certain departure time. In the second study we added an additional criterion: "travel time" and asked participants to find a fight option that offered a good trade-off between price and travel time. We found that using search tasks where participants need to decide between several competing search criteria (price vs. time) led to a higher search involvement and lower variance in usability and cognitive workload ratings between different CAs. We hope that our results will provoke discussion on how to make the evaluation of voice-only goal-oriented CAs more reliable and ecologically valid.
Testing educational achievements: from knowledge test to test of understanding

The article presents the results of author’s research in the field of assessing the formation of competencies of students in educational institutions of higher education using test technologies. The analysis carried out in the context of pedagogical qualimetry allows us to draw conclusions about the high didactic potential of individual test tasks with the ordering of answers. The study of the features of the application of test technologies in the formation of funds for assessment tools for ongoing educational programs of higher education revealed the advantage of this tool, expressed in the form of the ability to differentiate grades de-pending on the didactic goals of the test and the depth of control of educational achievements. In the article, it is scientifically grounded that the use of single test tasks with ordering of answers, unlike other types of tests (for example, with a single choice, multiple choice, establishing compliance, establishing a sequence), provides the developer with significantly more possibilities in forming grading scale and at the same time provides it with more interesting for analysis results of monitoring student performance. The authors of the article obtained initial analytical data, with the help of which quantitative characteristics can be calculated, which, together with the relevant criteria, can evaluate the success of the test solution. The method of questioning students who were trained according to the advanced training program of the faculty “Innovative Educational Technologies” received estimates of various types of test items, it was confirmed that the development of tests based on single test items with ordering of answers requires not only deep knowledge in his subject, but also creative abilities and general outlook. Recommendations on the introduction of test technologies in the educational environment are given.
Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing

Machine learning fairness concerns about the biases towards certain protected or sensitive group of people when addressing the target tasks. This paper studies the debiasing problem in the context of image classification tasks. Our data analysis on facial attribute recognition demonstrates (1) the attribution of model bias from imbalanced training data distribution and (2) the potential of adversarial examples in balancing data distribution. We are thus motivated to employ adversarial example to augment the training data for visual debiasing. Specifically, to ensure the adversarial generalization as well as cross-task transferability, we propose to couple the operations of target task classifier training, bias task classifier training, and adversarial example generation. The generated adversarial examples supplement the target task training dataset via balancing the distribution over bias variables in an online fashion. Results on simulated and real-world debiasing experiments demonstrate the effectiveness of the proposed solution in simultaneously improving model accuracy and fairness. Preliminary experiment on few-shot learning further shows the potential of adversarial attack-based pseudo sample generation as alternative solution to make up for the training data lackage.
Melody: Generating and Visualizing Machine Learning Model Summary to Understand Data and Classifiers Together

With the increasing sophistication of machine learning models, there are growing trends of developing model explanation techniques that focus on only one instance (local explanation) to ensure faithfulness to the original model. While these techniques provide accurate model interpretability on various data primitive (e.g., tabular, image, or text), a holistic Explainable Artificial Intelligence (XAI) experience also requires a global explanation of the model and dataset to enable sensemaking in different granularity. Thus, there is a vast potential in synergizing the model explanation and visual analytics approaches. In this paper, we present MELODY, an interactive algorithm to construct an optimal global overview of the model and data behavior by summarizing the local explanations using information theory. The result (i.e., an explanation summary) does not require additional learning models, restrictions of data primitives, or the knowledge of machine learning from the users. We also design MELODY UI, an interactive visual analytics system to demonstrate how the explanation summary connects the dots in various XAI tasks from a global overview to local inspections. We present three usage scenarios regarding tabular, image, and text classifications to illustrate how to generalize model interpretability of different data. Our experiments show that our approaches: (1) provides a better explanation summary compared to a straightforward information-theoretic summarization and (2) achieves a significant speedup in the end-to-end data modeling pipeline.
Practical and sample efficient zero-shot HPO

Zero-shot hyperparameter optimization (HPO) is a simple yet effective use of transfer learning for constructing a small list of hyperparameter (HP) configurations that complement each other. That is to say, for any given dataset, at least one of them is expected to perform well. Current techniques for obtaining this list are computationally expensive as they rely on running training jobs on a diverse collection of datasets and a large collection of randomly drawn HPs. This cost is especially problematic in environments where the space of HPs is regularly changing due to new algorithm versions, or changing architectures of deep networks. We provide an overview of available approaches and introduce two novel techniques to handle the problem. The first is based on a surrogate model and adaptively chooses pairs of dataset, configuration to query. The second, for settings where finding, tuning and testing a surrogate model is problematic, is a multi-fidelity technique combining HyperBand with submodular optimization. We benchmark our methods experimentally on five tasks (XGBoost, LightGBM, CatBoost, MLP and AutoML) and show significant improvement in accuracy compared to standard zero-shot HPO with the same training budget. In addition to contributing new algorithms, we provide an extensive study of the zero-shot HPO technique resulting in (1) default hyper-parameters for popular algorithms that would benefit the community using them, (2) massive lookup tables to further the research of hyper-parameter tuning.
Asymptotic relative submajorization of multiple-state boxes

Abstract Pairs of states, or “boxes” are the basic objects in the resource theory of asymmetric distinguishability (Wang and Wilde in Phys Rev Res 1(3):033170, 2019. 10.1103/PhysRevResearch.1.033170 ), where free operations are arbitrary quantum channels that are applied to both states. From this point of view, hypothesis testing is seen as a process by which a standard form of distinguishability is distilled. Motivated by the more general problem of quantum state discrimination, we consider boxes of a fixed finite number of states and study an extension of the relative submajorization preorder to such objects. In this relation, a tuple of positive operators is greater than another if there is a completely positive trace nonincreasing map under which the image of the first tuple satisfies certain semidefinite constraints relative to the other one. This preorder characterizes error probabilities in the case of testing a composite null hypothesis against a simple alternative hypothesis, as well as certain error probabilities in state discrimination. We present a sufficient condition for the existence of catalytic transformations between boxes, and a characterization of an associated asymptotic preorder, both expressed in terms of sandwiched Rényi divergences. This characterization of the asymptotic preorder directly shows that the strong converse exponent for a composite null hypothesis is equal to the maximum of the corresponding exponents for the pairwise simple hypothesis testing tasks.
Real Time Application for Automatic Object and 3D Position Detection and Sorting with Robotic Manipulator

This work deals with the likelihood of merging a 3D sensor into a robotic manipulator, with an objective to automatically detect, track and grasp an object, placing it in another location. To enhance the flexibility and easy functionality of the robot, MATLAB, a versatile and powerful programming language is used to control the robot. For this work, a common industrial task in many factories of pick and place is implemented. A robotic system consisting of an ABB IRB120 robot equipped with a gripper and a 3D Kinect for Windows camera sensor is used. The three-dimensional data acquisition, image processing and some different parameters of the camera are investigated. The information in the image acquired from the camera is used to determine the robot’s working space and to recognize workpieces. This information is then used to calculate the position of the objects. Using this information, an automatic path to grasp an object was designed and developed to compute the possible trajectory to an object in real time. To be able to detect the workpieces, object recognition techniques are applied using available algorithms in MATLAB’s Computer Vision Toolbox and Image Acquisition Toolbox. These give information about the position of the object of interest and its orientation. The information is therefore sent to the robot to create a path through a server-to-client connection over a computer network in real time.
Modeling User Behavior for Vertical Search: Images, Apps and Products

Search applications such as image search, app search and product search are crucial parts of web search, which we denote as vertical search services. This tutorial will introduce the research and applications of user behavior modeling for vertical search. The bulk of the tutorial is devoted to covering research into behavior patterns, user behavior models and applications of user behavior data to refine evaluation metrics and ranking models for web-based vertical search.
Work Stress Related Problems in Teachers of Social Studies in Government Schools in Qatar

This study aimed to identify the work stress experienced by teachers of social studies in government schools in the State of Qatar and the problems caused by it. The study used a descriptive-analytical method. The study sample consisted of (391) male and female teachers of social studies in the State of Qatar out of the study population of (968) who were selected using the random sampling method In order to achieve the goals of the study, a questionnaire was designed as a tool to collect data from the study sample. The tool was checked for validity and reliability prior to its execution. The results of the study included that teachers of social studies at all academic levels in the State of Qatar faced an average level of work stress. The study showed a statistically significant difference at the level of significance (α≤0.05) between the opinions of the members of the study sample in favor of females; and in favor of teachers who held Qatari nationality. No statistically significant differences were found at the level of significance (α≤0.05) according to the variable of the educational qualification, and according to the variables (job experience, marital status, academic level). The study also showed the existence of a number of problems resulting from work stress and the most important and highest administrative burdens and the lowest were to distinguish between teachers by nationality. This study recommends that the school authority should be delegated to teachers, according to their abilities and aptitudes, and to reduce their workload in order to enhance their professional efficiency.
Multimodal Dialogue State Tracking By QA Approach with Data Augmentation

Recently, a more challenging state tracking task, Audio-Video Scene-Aware Dialogue (AVSD), is catching an increasing amount of attention among researchers. Different from purely text-based dialogue state tracking, the dialogue in AVSD contains a sequence of question-answer pairs about a video and the final answer to the given question requires additional understanding of the video. This paper interprets the AVSD task from an open-domain Question Answering (QA) point of view and proposes a multimodal open-domain QA system to deal with the problem. The proposed QA system uses common encoder-decoder framework with multimodal fusion and attention. Teacher forcing is applied to train a natural language generator. We also propose a new data augmentation approach specifically under QA assumption. Our experiments show that our model and techniques bring significant improvements over the baseline model on the DSTC7-AVSD dataset and demonstrate the potentials of our data augmentation techniques.
Pseudo Random Binary Sequence Based on Cyclic Difference Set

With the increasing reliance on technology, it has become crucial to secure every aspect of online information where pseudo random binary sequences (PRBS) can play an important role in today’s world of Internet. PRBS work in the fundamental mathematics behind the security of different protocols and cryptographic applications. This paper proposes a new PRBS namely MK (Mamun, Kumu) sequence for security applications. Proposed sequence is generated by primitive polynomial, cyclic difference set in elements of the field and binarized by quadratic residue (QR) and quadratic nonresidue (QNR). Introduction of cyclic difference set makes a special contribution to randomness of proposed sequence while QR/QNR-based binarization ensures uniformity of zeros and ones in sequence. Besides, proposed sequence has maximum cycle length and high linear complexity which are required properties for sequences to be used in security applications. Several experiments are conducted to verify randomness and results are presented in support of robustness of the proposed MK sequence. The randomness of proposed sequence is evaluated by popular statistical test suite, i.e., NIST STS 800-22 package. The test results confirmed that the proposed sequence is not affected by approximations of any kind and successfully passed all statistical tests defined in NIST STS 800-22 suite. Finally, the efficiency of proposed MK sequence is verified by comparing with some popular sequences in terms of uniformity in bit pattern distribution and linear complexity for sequences of different length. The experimental results validate that the proposed sequence has superior cryptographic properties than existing ones.
Analysis of Chemical Laboratory Management at SMAN 55 Jakarta

An understanding of laboratory management is highly important to be owned by parties related to the laboratory, both directly and indirectly. Laboratories must be managed and utilized properly, because chemical laboratories are one type of laboratory that is considered quite dangerous in the context of carrying out education, research, and / or community service. This study aims to describe the management of a chemical laboratory using qualitative descriptive research methods. Research subjects were the principal, vice chairman of the curriculum, chair of the laboratory, and teachers in the field of chemistry studies. Data collection techniques are observation, interviews, and documentation. The results of the research are: 1) Planning of chemical laboratory work program has been prepared and standardized by the head of the laboratory, 2) Organizing the laboratory structurally already exists, 3) Organizing by the principal involving all those involved in laboratory management, 4) The implementation of the program is suitable with the plan of the work program, 5) Supervision and evaluation of the principal must still be carried out intensively. In other words, laboratory management at SMA 55 Jakarta has been well organized.
Sarcasm in Sentiment Analysis of Indonesian Text: A Literature Review

Social media, blogs and online groups become a forum that makes is easy for the Indonesian people to express their opinions, suggestions, complaints and even criticisms of a subject liberally. Sentiment analysis is a method for classifying positive, neutral, and negative polarity of the opinions that expressed by the internet users. Sarcasm is one of the challenges to classsifying the sentiments of an opinion. This research is a literature review to examine several studies to find out the methods for detecting sarcasm and to know the effect of sarcasm on the sentiment classification accuracy. The result of this literature review can be used as a reference for developing the sarcasm detection methods.
Experimental semi-autonomous eigensolver using reinforcement learning

Abstract The characterization of observables, expressed via Hermitian operators, is a crucial task in quantum mechanics. For this reason, an eigensolver is a fundamental algorithm for any quantum technology. In this work, we implement a semi-autonomous algorithm to obtain an approximation of the eigenvectors of an arbitrary Hermitian operator using the IBM quantum computer. To this end, we only use single-shot measurements and pseudo-random changes handled by a feedback loop, reducing the number of measures in the system. Due to the classical feedback loop, this algorithm can be cast into the reinforcement learning paradigm. Using this algorithm, for a single-qubit observable, we obtain both eigenvectors with fidelities over 0.97 with around 200 single-shot measurements. For two-qubits observables, we get fidelities over 0.91 with around 1500 single-shot measurements for the four eigenvectors, which is a comparatively low resource demand, suitable for current devices. This work is useful to the development of quantum devices able to decide with partial information, which helps to implement future technologies in quantum artificial intelligence.