Reviewerly/exHarmony huggingface dataset:
Allocating Opportunities in a Dynamic Model of Intergenerational Mobility (Extended Abstract)

Opportunities such as higher education can promote intergenerational mobility, leading individuals to achieve levels of socioeconomic status above that of their parents. In this work, which is an extended abstract of a longer paper in the proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, we develop a dynamic model for allocating such opportunities in a society that exhibits bottlenecks in mobility; the problem of optimal allocation reflects a trade-off between the benefits conferred by the opportunities in the current generation and the potential to elevate the socioeconomic status of recipients, shaping the composition of future generations in ways that can benefit further from the opportunities. We show how optimal allocations in our model arise as solutions to continuous optimization problems over multiple generations, and we find in general that these optimal solutions can favor recipients of low socioeconomic status over slightly higher-performing individuals of high socioeconomic status --- a form of socioeconomic affirmative action that the society in our model discovers in the pursuit of purely payoff-maximizing goals. We characterize how the structure of the model can lead to either temporary or persistent affirmative action, and we consider extensions of the model with more complex processes modulating the movement between different levels of socioeconomic status.
Application of Neurosymbolic AI to Sequential Decision Making

In the history of AI, two main paradigms have been proposed to solve Sequential Decision Making (SDM) problems: Automated Planning (AP) and Reinforcement Learning (RL). Among the many proposals to unify both fields, the one known as neurosymbolic AI has recently attracted great attention. It combines the Deep Neural Networks used in modern RL with the symbolic representations typical of AP. The main goal of this PhD is to progress the state of the art in neurosymbolic AI for SDM, developing methods for both solving these problems and learning aspects of their structure.
Learning Realistic Patterns from Visually Unrealistic Stimuli: Generalization and Data Anonymization (Extended Abstract)

Good training data is a prerequisite to develop useful Machine Learning applications. However, in many domains existing data sets cannot be shared due to privacy regulations (e.g., from medical studies). This work investigates a simple yet unconventional approach for anonymized data synthesis to enable third parties to benefit from such anonymized data. We explore the feasibility of learning implicitly from visually unrealistic, task-relevant stimuli, which are synthesized by exciting the neurons of a trained deep neural network. As such, neuronal excitation can be used to generate synthetic stimuli. The stimuli data is used to train new classification models. Furthermore, we extend this framework to inhibit representations that are associated with specific individuals. Extensive comparative empirical investigation shows that different algorithms trained on the stimuli are able to generalize successfully on the same task as the original model.
Domain Generalization through the Lens of Angular Invariance

Domain generalization (DG) aims at generalizing a classifier trained on multiple source domains to an unseen target domain with domain shift. A common pervasive theme in existing DG literature is domain-invariant representation learning with various invariance assumptions. However, prior works restrict themselves to an impractical assumption for real-world challenges: If a mapping induced by a deep neural network (DNN) could align the source domains well, then such a mapping aligns a target domain as well. In this paper, we simply take DNNs as feature extractors to relax the requirement of distribution alignment. Specifically, we put forward a novel angular invariance and the accompanied norm shift assumption. Based on the proposed term of invariance, we propose a novel deep DG method dubbed Angular Invariance Domain Generalization Network (AIDGN). The optimization objective of AIDGN is developed with a von-Mises Fisher (vMF) mixture model. Extensive experiments on multiple DG benchmark datasets validate the effectiveness of the proposed AIDGN method.
Dynamic Bandits with Temporal Structure

In this work, we study a dynamic multi-armed bandit (MAB) problem, where the expected reward of each arm evolves over time following an auto-regressive model. We present an algorithm whose per-round regret upper bound almost matches the regret lower bound, and numerically demonstrate its efficacy in adapting to the changing environment.
A Solver + Gradient Descent Training Algorithm for Deep Neural Networks

We present a novel hybrid algorithm for training Deep Neural Networks that combines the state-of-the-art Gradient Descent (GD) method with a Mixed Integer Linear Programming (MILP) solver, outperforming GD and variants in terms of accuracy, as well as resource and data efficiency for both regression and classification tasks. Our GD+Solver hybrid algorithm, called GDSolver, works as follows: given a DNN D as input, GDSolver invokes GD to partially train D until it gets stuck in a local minima, at which point GDSolver invokes an MILP solver to exhaustively search a region of the loss landscape around the weight assignments of D’s final layer parameters with the goal of tunnelling through and escaping the local minima. The process is repeated until desired accuracy is achieved. In our experiments, we find that GDSolver not only scales well to additional data and very large model sizes, but also outperforms all other competing methods in terms of rates of convergence and data efficiency. For regression tasks, GDSolver produced models that, on average, had 31.5% lower MSE in 48% less time, and for classification tasks on MNIST and CIFAR10, GDSolver was able to achieve the highest accuracy over all competing methods, using only 50% of the training data that GD baselines required.
Strategi Pemasaran Penerimaan Mahasiswa Baru Pasca Perubahan Bentuk Dari Akademi Manajemen Belitung Ke Politeknik Belitung

New student admissions play an important role in the continuity and teaching process in universities, especially at Politeknik Belitung. Improving the quality of university in terms of the quality of education and services greatly affects the confidence level of new students to choose Politeknik Belitung as the university where they study. One way to improve the quality of higher education is to upgrade deformed from Academy to a Politeknik, along with increase number of study programs. Planning a good marketing strategy concept and implementing the right marketing strategy will play an important role in achieving the target number of new student admissions. This article shows several strategies that can be used by the Politeknik Belitung through the new student admissions committee including by conducting socialization, using social media and utilizing existing resources by utilizing institutions, communities and people who are connected to the Politeknik Belitung, especially alumni who have completed education at Politeknik Belitung
Construction and Application of Event Logic Graph: A Survey

Since being proposed in 2017, event logic graph has attracted more and more researchers' attention and has been gradually applied in various fields such as finance, health care, transportation, information, politics, etc. Unlike the traditional knowledge graph describing static entities and their attributes and relationships, the event logic graph describes the evolution rules and patterns between events. The construction of event logic graphs is significant for understanding human behavior patterns and mining event evolution rules. The survey first systematically combs the work of constructing an event logic graph, including event extraction and event relationship extraction methods. Secondly, the typical application of the event logic graph is explained. Finally, the challenges of event logic graph construction are analyzed, and future research trends are prospected.
On the Enlightenment of “MOOC” to the Cultivation of English Majors

With the advent of the era of globalization and informationization, the opening and sharing of global higher education resources has become the trend of the times. As an emerging form of open education, " Mooc" takes the network as a platform, with college students as the main body, and is open to all members of society for free, and is receiving widespread attention and favor. How to use this effective platform to build English majors in colleges and universities, cultivate compound talents, and improve teachers' teaching level is the central topic of this paper.
Sosialisasi Literasi Digital bagi Siswa Sekolah Menengah Atas Negeri 3 Bukittinggi

This community service regarding the socialization of digital literacy aims to raise awareness of the importance of digital literacy to high school (SMA) students. Digital literacy is the ability to process and sort information to minimize the negative impact of receiving incorrect information if it filtered first, especially in the digital era and globalization, where the flow of information is so fast and easy to access due to the narrowing of space and time due to technological developments. This service activity is the growth of essential digital literacy skills for the target group of participants. This ability is characterized by the ability to sort between true and false information by looking for supporting data for that information. This can be seen in the last series of activities in determining which information is accurate and false.
KEBUTUHAN DAN KETERSEDIAAN AIR DI DAERAH IRIGASI WARUNGKIARA

Water is one of the natural resources and an important element to support the sustainability of life on earth. Agriculture is an important sector that requires water to support food supplies. The existence of an uneven distribution of potential water resources will result in agricultural yields that are not optimal on land with insufficient water availability. Therefore, it is necessary to calculate the need and availability of water in the Warungkiara Irrigation Area. The method used in this research is the Mock method. The results obtained from the study are that the highest value occurred in July period 2 with a figure of 1,510 m3/sec available discharge. This means that in that month agricultural land really needs a lot of water.
A hybrid differential evolution approach to designing deep convolutional neural networks for image classification

© Springer Nature Switzerland AG 2018. Convolutional Neural Networks (CNNs) have demonstrated their superiority in image classification, and evolutionary computation (EC) methods have recently been surging to automatically design the architectures of CNNs to save the tedious work of manually designing CNNs. In this paper, a new hybrid differential evolution (DE) algorithm with a newly added crossover operator is proposed to evolve the architectures of CNNs of any lengths, which is named DECNN. There are three new ideas in the proposed DECNN method. Firstly, an existing effective encoding scheme is refined to cater for variable-length CNN architectures; Secondly, the new mutation and crossover operators are developed for variable-length DE to optimise the hyperparameters of CNNs; Finally, the new second crossover is introduced to evolve the depth of the CNN architectures. The proposed algorithm is tested on six widely-used benchmark datasets and the results are compared to 12 state-of-the-art methods, which shows the proposed method is vigorously competitive to the state-of-the-art algorithms. Furthermore, the proposed method is also compared with a method using particle swarm optimisation with a similar encoding strategy named IPPSO, and the proposed DECNN outperforms IPPSO in terms of the accuracy.
Secure and efficient software implementation of QC-MDPC code-based cryptography

The emergence of quantum computers is pushing an unprecedented transition in the public key cryptography field. Algorithms in the current standard are vulnerable to attacks using quantum computers and need, therefore, to be replaced. Cryptosystems based on error-correcting codes are considered some of the most promising candidates to replace them for encryption schemes. Among the code families, QC-MDPC codes achieve the smallest key sizes while maintaining the desired security properties. Their performance, however, still needs to be greatly improved to reach a competitive level. In this work, we optimize the performance of QC-MDPC code-based cryptosystems through improvements concerning both their implementations and algorithms. We first present a new enhanced version of QcBits’ key encapsulation mechanism, which is a constant time implementation of the Niederreiter cryptosystem using QCMDPC codes. Comparing with the current state-of-the-art, the BIKE implementation, our code performs 1.9 times faster when decrypting messages. We then optimize the performance of QC-MDPC code-based cryptosystems through the insertion of a configurable failure rate in their arithmetic procedures. Using a failure rate negligible compared to the security level (2􀀀128), we achieve speedups of 1.6 to 2 times in some arithmetic algorithms. By inserting these algorithms in our enhanced version of QcBits, we were able to achieve a speedup of 1.9 on the key generation and up to 1.4 on the decryption time. Comparing with BIKE, our final version of QcBits performs the uniform decryption 2.7 times faster. Moreover, the techniques presented in this work can also be applied to BIKE, opening new possibilities for further improvements.
Converting Symmetric Cryptography to SAT Problems Using Model Checking Tools

Algebraic attack is a recent technique used to break symmetric cryptography including Crypto-1, which is used in smart NFC cards around the world. The main method is to convert a cryptography algorithm to a system of equations over a finite field, to translate it into a SAT problem and to use a good solver. The main advantage of converting cryptography problems into SAT is that we can use a lot of well know tools for SAT solving and test it in an instance. However, the high degree labor intensiveness on converting cryptography to SAT instances is a difficult task for advancement of research. To pursue this problem, this work presents a technique to quickly translate symmetric cryptography into SAT problems using Bounded Model Checking (BMC) tools. Computational experiments are performed, converting the RC4 and AES algorithms into a SAT problem.
The First Biclique Cryptanalysis of Serpent-256

The Serpent cipher was one of the finalists of the AES process and as of today there is no method for finding the key with fewer attempts than that of an exhaustive search of all possible keys, even when using known or chosen plaintexts for an attack. This work presents the first two biclique attacks for the full-round Serpent-256. The first uses a dimension 4 biclique while the second uses a dimension 8 biclique. The one with lower dimension covers nearly 4 complete rounds of the cipher, which is the reason for the lower time complexity when compared with the other attack (which covers nearly 3 rounds of the cipher). On the other hand, the second attack needs a lot less pairs of plaintexts for it to be done. The attacks require 2255.21 and 2255.45 full computations of Serpent-256 using 288 and 260 chosen ciphertexts respectively with negligible memory.
Character Recognition Using Neural Network Self- Organizing Map (NN-SOM)

Understanding the neuron-logic and natural sense with which humans can recognize textual pattern and characters had actually called for computational artifact to mimic human behaviour.Character recognition is a key part of applying processing to safeguard transcribed data in order to recover it at a later stage, just as encouraging its method of correspondence utilizing computational intelligence and data mining approach.Character recognition with computing gadget allows easy access, content storage and distributed capacity.In this work, self-organizing map of the neural network was used to distinguish alphabetic characters by assigning them to different bins; using the ASCII values to represent each of the graphic characters and train the network with anticipated responses to recognize them.Each line has a 5x7 dot representation of each character with simple 3-bit representation; each of the output categories was named as well with a binary map of 35 pixel values.The simulator completed the learning in fewer cycles, and training patterns were learned very well while smaller tolerance was used.The results showed each foreign character in the match category closest to it.The middle layer of the network acts as a feature detector with much influence on training time and generalization capability.
THE USE OF SQ4R FOR IMPROVING READING SKILL: HOW SHOULD IT BE IMPLEMENTED?

For senior high school students, understanding literature has become a struggle. Impaired vocabulary and prior knowledge, grammatical flaws, and poor reading methods were all important factors in reading. As a result, this study aims to use SQ4R as a technique for overcoming learners’ difficulties in learning reading, particularly those studying English in SMKN 1 Bakung. SQ4R is one of the most widely used acronyms (Survey, Question, Read, Recite, Review, and Reflect). The SQ4R reading method is aimed to engage kids in reading activities, encourage them to enjoy reading, and enhance their academic achievement as a result. Students should apply what they already know to assist them learn new material, according to SQ4R. This study employs Classroom Action Research (CAR) at XI TKJ, using data obtained from two instruments: a post-test and observation sheets. A total of 26 students participated in the study. The findings of this study show that after being taught the approach, students’ reading ability improves. The students acquire an average score of 55.4 in pre-test. Then, their average score increases to 63.3 in the first cycle. The average score climbs to 73.3 average scores in the second round. The success criteria for this study were that 75% of the students (19 students) got a score of 65.
Peningkatan Manajemen Informasi Organisasi dengan Memanfaatkan Website

Girirupo Youth Generation is a social organization that has the aim to advance the village of Girirupo in particular and all levels of society in general. The organization has internal and external activities. Internal activities involve active members of the organization, while external activities involve non-governmental organizations. In business processes these organizations have a lot of data that is processed into information. The management of this information is carried out by recording regularly using books. The information generated is passed on to members and the community through word of mouth, instant messaging services, as well as WhatsApp groups owned by the organization. Management and delivery of information has not been done in a structured. The ability of organizations to manage data and information becomes more effective and structured after training in information management. The ease and speed in delivering information can also be realized by having an organization's website, namely girirupomuda.com.
Simple Linear Regression

Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.
MARKET BASKET ANALYST BASED ON WEBSITE USING ECLAT ALGORITHM (CASE STUDY POLA PHARMACY)

Market basket analyst is a data mining technique todiscover associations between datasets. Association rulemining identifies a relationship between a large set ofdata item. When a large quantity of data is constantlyobtained and stored in database, several industries arebecoming concerned about mining association rulesfrom their databases. Market basket analysis examinescustomer buying patterns by identifying associationsamong various items that customers place in theirshopping baskets. It is helpful to examine customerpurchasing behaviour and assists in increasing sales. Sothis system is intended to develop a system for marketbasket analysis on Pola Pharmacy which generateassociation rules among itemsets with the use of ECLAT(Equivalence Class Transformation) algorithm. Thissystem supports the decision making process for amarket expert.
PEMBUATAN GAME PUZZLE “DIGITUS” BERBASIS ANDROID

In this game, player is asked to drag arrow to their pair. There are 2 modes in this game, i.e. one hand and two hands. 10 different stages for each mode. This game use multitouch features. Stage 2 to 10 have a minimum point to be able to open and play the next stage. This game has been tested by black box method, alpha testing and beta testing using a questionnaire through Google Form and Google drive to save Digitus game files. The results show that Digitus is fascinating to be played over and over again, how to play feature can be understood easily, and attractive design.
Pembuatan Game Musical Puzzle “Music Puzzle” Pada Platform Android

“Music Puzzle” is a musical puzzle game for Android. Player will try to arrange the puzzle piece by listening to the music in each puzzle and correct the order of the music. This game use classical music as the theme of the game. This game is developed by Unity game engine with C# as the programming language. Testing has been done with Black Box Testing, Alpha Testing by lecturer and Beta Testing by survey to 30 respondents. The results show that the “Music Puzzle” is an interesting game to play and encourage respondents to interest in classical music.
PEMBUATAN GAME HACK AND SLASH 2D “SERENADE TOWER”

“Serenade Tower” is a hack-and-slash game build for PC platform. Player will play as Hero to slay monster and destroy a tower called Serenade Tower which cause misfortune. “Serenade Tower” use keyboard as the main control to character movement such as moving, attacking and using skill. This game created by Unity game engine with C# as the programming language. Testing has been done by Black Box Testing, Alpha Testing by lecturer and Beta Testing by sharing and doing survey to 30 respondent. The results is the game have neat and nice user interface with balanced gameplay.