Reviewerly/exHarmony huggingface dataset:
Efficient Querying from Weighted Binary Codes

Binary codes are widely used to represent the data due to their small storage and efficient computation. However, there exists an ambiguity problem that lots of binary codes share the same Hamming distance to a query. To alleviate the ambiguity problem, weighted binary codes assign different weights to each bit of binary codes and compare the binary codes by the weighted Hamming distance. Till now, performing the querying from the weighted binary codes efficiently is still an open issue. In this paper, we propose a new method to rank the weighted binary codes and return the nearest weighted binary codes of the query efficiently. In our method, based on the multi-index hash tables, two algorithms, the table bucket finding algorithm and the table merging algorithm, are proposed to select the nearest weighted binary codes of the query in a non-exhaustive and accurate way. The proposed algorithms are justified by proving their theoretic properties. The experiments on three large-scale datasets validate both the search efficiency and the search accuracy of our method. Especially for the number of weighted binary codes up to one billion, our method shows a great improvement of more than 1000 times faster than the linear scan.
EPOC: Efficient Perception via Optimal Communication

We propose an agent model capable of actively and selectively communicating with other agents to predict its environmental state efficiently. Selecting whom to communicate with is a challenge when the internal model of other agents is unobservable. Our agent learns a communication policy as a mapping from its belief state to with whom to communicate in an online and unsupervised manner, without any reinforcement. Human activity recognition from multimodal, multisource and heterogeneous sensor data is used as a testbed to evaluate the proposed model where each sensor is assumed to be monitored by an agent. The recognition accuracy on benchmark datasets is comparable to the state-of-the-art even though our model uses significantly fewer parameters and infers the state in a localized manner. The learned policy reduces number of communications. The agent is tolerant to communication failures and can recognize unreliable agents through their communication messages. To the best of our knowledge, this is the first work on learning communication policies by an agent for predicting its environmental state.
Generative Exploration and Exploitation

Sparse reward is one of the biggest challenges in reinforcement learning (RL). In this paper, we propose a novel method called Generative Exploration and Exploitation (GENE) to overcome sparse reward. GENE automatically generates start states to encourage the agent to explore the environment and to exploit received reward signals. GENE can adaptively tradeoff between exploration and exploitation according to the varying distributions of states experienced by the agent as the learning progresses. GENE relies on no prior knowledge about the environment and can be combined with any RL algorithm, no matter on-policy or off-policy, single-agent or multi-agent. Empirically, we demonstrate that GENE significantly outperforms existing methods in three tasks with only binary rewards, including Maze, Maze Ant, and Cooperative Navigation. Ablation studies verify the emergence of progressive exploration and automatic reversing.
Translational Bit-by-Bit Multi-bit Quantization for CRNN on Keyword Spotting

Keyword spotting (KWS) is one of automatic speech recognition (ASR) research fields. KWS algorithms based on neural network are remarkably better than others. It is extremely suitable for KWS algorithms to deploy on portable hardware devices to achieve excellent performance, but the number of weights is too large to apply due to limited hardware storage resources. Quantization is a natural approach to solve this puzzle. We propose a quantization formula to deploy KWS algorithms based on neural network on hardware devices effectively. In this paper, we consider symmetry of weights about the origin, and reorganize the order of 1-bit quantization of residual error and alternating quantization of minimization, proposing our translational bit-by-bit multi-bit quantization. Furthermore, we implement our method to convolutional recurrent neural network (CRNN) for KWS problem, analyzing results on two aspects, root mean square error (RMSE) and accuracy respectively. We can obtain smaller RMSE between quantized weights and full precision weights, the cost of it is the increment of computational complexity, but we can execute quantization in every mini-batch to constrain training time within a reasonable range. Our formula can get an acceptable accuracy by 3-bit quantization, with ~10.65× memory saving. And we can obtain no loss accuracy comparing to full precision by 4-bit quantization, sometimes even better, with ~7.99× memory saving. Our quantization method can achieve better performance and save memory resources, which are able to be used to deploy KWS algorithms based on neural network on hardware devices effectively.
Just Add Functions: A Neural-Symbolic Language Model

Neural network language models (NNLMs) have achieved ever-improving accuracy due to more sophisticated architectures and increasing amounts of training data. However, the inductive bias of these models (formed by the distributional hypothesis of language), while ideally suited to modeling most running text, results in key limitations for today's models. In particular, the models often struggle to learn certain spatial, temporal, or quantitative relationships, which are commonplace in text and are second-nature for human readers. Yet, in many cases, these relationships can be encoded with simple mathematical or logical expressions. How can we augment today's neural models with such encodings?In this paper, we propose a general methodology to enhance the inductive bias of NNLMs by incorporating simple functions into a neural architecture to form a hierarchical neural-symbolic language model (NSLM). These functions explicitly encode symbolic deterministic relationships to form probability distributions over words. We explore the effectiveness of this approach on numbers and geographic locations, and show that NSLMs significantly reduce perplexity in small-corpus language modeling, and that the performance improvement persists for rare tokens even on much larger corpora. The approach is simple and general, and we discuss how it can be applied to other word classes beyond numbers and geography.
Evaluation of DLX Microprocessor Instructions Efficiency for Image Compression

Internet of Things (IoT) implementation usually uses a generic microprocessor, especially when the implementation is for a specific functionality where ASIC (Application-specific Integrated Circuit) has not yet available. However, a generic microprocessor originally designed for general purpose application that uses many machine instructions, which in return causes an inefficient power consumption. Although ASIC is the best choice when comes to power efficiency, ASIC is hardcoded, meaning that it is not programmable or is not flexible for future enhancement or to accommodate intelligent properties like ability to learn and grow. This research tries to find the minimum instruction set to propose an ASIP (Application-specific Instruction Set Processor) for application in high quality image compression coming from CCTV. This research uses the DLX microprocessor instruction set as a reference on Huffman Coding as part of JPEG compression. The research shows that Huffman Coding needs only 9 type of machine instruction, they are ADD, ADDI, SUBI, LW, SW, BEQZ, BNEZ, SEQ, and SGT.
Effect of fuzzy-membership function types on the search capability of interactive search algorithm

Fuzzy logic is widely used as the fundamental platform for the many artificial intelligence-based systems like control of subways, air conditioners, transmission systems, facial pattern recognition, antiskid braking systems.In the theory of fuzzy set, membership functions (MFs) provide an infrastructure for determining the degree of truth in a fuzzy model.So, they can play important role in the performance of these logical mechanisms.Different types of MF can be defined to fuzzification-defuzzification process (i.e.converting Boolean input to a fuzzy output and vise-versa).They are categorized and named based on the shape of their diagrams as triangular, Gaussian, trapezoidal and so forth.In the current study the effect of MF's type on the search capability of Interactive Search Algorithm (ISA) is assessed.To meet this aim, a metaheuristic technique that are known as Interactive Search Algorithm and strengthen with fuzzy adjustment mechanism is tested on solving a number of benchmark problems involving different types of MFs.The results indicate that due to stochastic essence of metaheuristic approaches the type of MF does not seriously affect the search capability of this technique.Therefore, it can be stated that the MFs should be selected based on the simplicity criterion.In other words, the simplest MF which provides the designers requirements can be the best choice.
Radial and Directional Posteriors for Bayesian Deep Learning

We propose a new variational family for Bayesian neural networks. We decompose the variational posterior into two components, where the radial component captures the strength of each neuron in terms of its magnitude; while the directional component captures the statistical dependencies among the weight parameters. The dependencies learned via the directional density provide better modeling performance compared to the widely-used Gaussian mean-field-type variational family. In addition, the strength of input and output neurons learned via our posterior provides a structured way to compress neural networks. Indeed, experiments show that our variational family improves predictive performance and yields compressed networks simultaneously.
Improving BDD Enumeration for LWE Problem Using GPU

In this paper, we present a GPU-based parallel algorithm for the Learning With Errors (LWE) problem using a lattice-based Bounded Distance Decoding (BDD) approach. To the best of our knowledge, this is the first GPU-based implementation for the LWE problem. Compared to the sequential BDD implementation of Lindner-Peikert and pruned-enumeration strategies by Kirshanova [1], our GPU-based implementation is almost faster by a factor 6 and 9 respectively. The used GPU is NVIDIA GeForce GTX 1060 6G. We also provided a parallel implementation using two GPUs. The results showed that our algorithm is scalable and faster than the sequential version (Lindner-Peikert and pruned-enumeration) by a factor of almost 13 and 16 respectively. Moreover, the results showed that our parallel implementation using two GPUs is more efficient than Kirshanova et al.'s parallel implementation using 20 CPU-cores.
Hybrid Particle Filter Based Dynamic Compressed Sensing for Signal-Level Multitarget Tracking

We propose a novel algorithm, state propagation based dynamic compressed sensing (SP-DCS), that uses a target dynamic model in dynamic compressed sensing (DCS) to track a fixed number of targets. To track a time-varying number of targets using raw measurements from a Doppler radar, we also propose a novel hybrid particle filter based dynamic compressed sensing (HPF-DCS) algorithm. We calculate the support set in a Bayesian framework and a particle filter approximates the posterior probability mass function (pmf) of the support set. HPF-DCS is a combination of random and deterministic sampling. In random sampling, a number of predicted existing sub-particles are sampled from the prior pmf of the existing support set to handle the scenario when targets disappear randomly at a scan time. In deterministic sampling, the new support set corresponding to newly appearing targets is calculated by solving a sparsity promoting optimization problem. Our simulation results show that the proposed algorithm can track a time-varying number of targets successfully. It also outperforms the sequential Monte Carlo based probability hypothesis density (SMC-PHD) filter, as well as the multi-mode, multi-target track before detect (MM-MT-TBD) filter.
Aplikasi Pembelajaran IPA Tentang Fauna Model Game untuk Sekolah Dasar dengan Metode Computer Assisted Instruction (CAI)

Learning in an educational institution is considered as the initial stage of the formation of identity or the basic process of education, especially in primary schools. At this time students experience educational and learning process activities should be very fun in practicing the catchment of students. For this reason, it is necessary to make a media for introducing fauna recognition game models that are available in science subjects in grade 3 elementary school. This game is a learning game with fauna introduction, fauna needs and fauna grouping. The computer is one of the tools and is a technology product that is considered to have the ability to produce learning products that are integrated with CAI (Computer Assisted Instruction), CAI has many types for the learning process including drill and practice, simulation, tutorials, and games that have been developed. In this study, learning applications were made that apply one of the CAI (Computer Assisted Instruction) methods in the form of games to increase students' interest in learning. With the help of adobe flash professional CS6 software, the application in this study was created. It is hoped that the application of the learning model of natural science games with the CAI (Computer Assisted Instruction) method can help the learning process and understanding of the material.
Software Engineering for Big Data Application Development: Systematic Literature Survey Using Snowballing

The Big Data Analytics is revolutionizing the Information and Technology sector. Researchers and corporate giants are investing their resources into developing big data application softwares that can provide them useful insight about collected data efficiently and help them to make strategic decisions. But failure rates for big data applications is rather high than anticipated. Hence there is a need to revisit software engineering methodologies and software processes and make them more adaptable for Big Data Applications. Objective: In this paper we are doing systematic literature review of research papers that focuses on software engineering methodologies and software processes for big data applications. Method: Total of 695 papers were reviewed and 22 papers were found relevant according to research question/s using snowballing method. Result: The research papers helped in finding the methodologies and requirements for developing big data applications.
GOAL TREES AS STRUCTURING ELEMENT IN A DIGITAL DATA-DRIVEN STUDY ASSISTANT

The Future Skills Report about the future of learning and higher Education (Ehlers & Kellermann, 2019) defines a variety of skills in which the active learner plays a central role.Starting from this perspective, our idea is to promote future skills with a digital data-driven study assistant for.As a theoretical foundation research about constructivism, self-regulation and goal-setting is reviewed and the concept of Goal Trees as structuring element of a software is derived.On a psychological level we show how Goal Trees may increase motivation and simplify study planning.On the level of user interface design, we show how Goal Trees may be used as structuring element of a digital data-driven study assistant.Consequently we outline how functionalities derived from research about goal-setting and self-regulation can be implemented in a study assistant.Finally advantages and challenges of implementing such a system are discussed.
Local Differential Privacy for Evolving Data

&#x0D; &#x0D; &#x0D; There are now several large scale deployments of differential privacy used to collect statistical information about users. However, these deployments periodically recollect the data and recompute the statistics using algorithms designed for a single use. As a result, these systems do not provide meaningful privacy guarantees over long time scales. Moreover, existing techniques to mitigate this effect do not apply in the “local model” of differential privacy that these systems use.&#x0D; In this paper, we introduce a new technique for local differential privacy that makes it possible to maintain up-to-date statistics over time, with privacy guarantees that degrade only in the number of changes in the underlying distribution rather than the number of collection periods. We use our technique for tracking a changing statistic in the setting where users are partitioned into an unknown collection of groups, and at every time period each user draws a single bit from a common (but changing) group-specific distribution. We also provide an application to frequency and heavy-hitter estimation.&#x0D; &#x0D; &#x0D;
Investigating Developer Perception on Test Smells Using Better Code Hub : work in progress

Test smells can be found in test code using a variety of tools. In this paper, we present our integration of a test smell detection tool into Better Code Hub (BCH), an online environment for monitoring code quality and identifying problems in it. We extended BCH with test smell detection and observe how developers react to various instances of test smells. By integrating this detection into BCH, we gain access to a wide range of developers working on both open-source and commercial projects using their own code. We study whether developers consider these test smells important and what they do with them in future code changes. From our preliminary results, we found out a high test smell detection accuracy for most test smells; however, developers are only willing to remove a small portion of them.
Pengaruh Model Pembelajaran Jarak Jauh Berbasis Google Classroom

The government has set up distance education based on Law No. 20 Year 2003 About Educational system (SISDIKNAS), which were referred to the distance education is education that the students are apart from educators and learning to use various sources of learning through technology, general problems education in Indonesia, the learning system is not good, seen from the learning methods, instructional media, and materials that are still not appropriate in their use. It is causing the low results of learning of students, which is influenced by the various methods of learning are less precise. The method used in this study is an experimental method. The form of this research is in the form of pre-test and post-test of the experimental group and the control group to determine student learning outcomes. From the average value of the pre-test of the control class and experiment, with the post-test value of the control class and experiment. Rated Average grade control in the pre-test is worth 75.16 and the post-test worth 70.28. there is a decrease in the average value of the conversation class by 4.88. While the value of the average class experiment in pre-test is worth 77.13 and the post-test worth 88.00.  thus, an increase in the value of the average grade of experiments at 10.87. it means that there is an increase in the value of the average after the learning using Google Classroom, although not too significant.
User Intent Inference for Web Search and Conversational Agents

User intent understanding is a crucial step in designing both conversational agents and search engines. Detecting or inferring user intent is challenging, since the user utterances or queries can be short, ambiguous, and contextually dependent. To address these research challenges, my thesis work focuses on: 1) Utterance topic and intent classification for conversational agents 2) Query intent mining and classification for Web search engines, focusing on the e-commerce domain. To address the first topic, I proposed novel models to incorporate entity information and conversation-context clues to predict both topic and intent of the user's utterances. For the second research topic, I plan to extend the existing state of the art methods in Web search intent prediction to the e-commerce domain, via: 1) Developing a joint learning model to predict search queries' intents and the product categories associated with them, 2) Discovering new hidden users' intents. All the models will be evaluated on the real queries available from a major e-commerce site search engine. The results from these studies can be leveraged to improve performance of various tasks such as natural language understanding, query scoping, query suggestion, and ranking, resulting in an enriched user experience.
Investigating Causes of Scalability Challenges in Agile Software Development from a Design Perspective

The aim of this work is to identify and discuss causes of scalability challenges in the core of Agile software development and project management, which pertains to the Agile Manifesto in general and to the Scrum Guide in particular. First, the causes of challenges are identified with an analysis on the principles of the Agile Manifesto and the Scrum Guide, and on the results of a literature review conducted in the scope of the study. Then, the following causes of challenges are evaluated with our discussions and evidence from the literature where available: Physical dependencies, fragmentation and feudalism, short and static events, narrow focus on product, narrow focus on construction, and bottlenecks from one to many relations. The most significant contribution of this study is a thorough description of the causes of scaling challenges that are inherent in the core of Agile software development from a design perspective.
Adversarial Network with Dual U-net Model and Multiresolution Loss Computation for Medical Images Registration

This paper presents a generative adversarial networks (DUGan) for images registration, which is unsupervised without ground-truth deformations. In this registration framework, the generative network is in charge of generating the deformation field, which is the input to the transformation module, whose output is the wrapped images. The wrapped and fixed images pair will be judged the similarity by the discriminator network. The first innovation is that the U-Net model is introduced in the two networks making use of its hierarchical advantages. And the second one is that multiresolution loss computation is adopted to keep the deformation smoothness in the generative network, and guarantee the efficient similarity in the discriminator network. Experiments on brain datasets indicate that our method yields comparable accuracy to state-of-the-art registration methods.