Reviewerly/exHarmony huggingface dataset:
Autoencoders as Weight Initialization of Deep Classification Networks for Cancer versus Cancer Studies

Cancer is still one of the most devastating diseases of our time. One way of automatically classifying tumor samples is by analyzing its derived molecular information (i.e., its genes expression signatures). In this work, we aim to distinguish three different types of cancer: thyroid, skin, and stomach. For that, we compare the performance of a Denoising Autoencoder (DAE) used as weight initialization of a deep neural network. Although we address a different domain problem in this work, we have adopted the same methodology of Ferreira et al.. In our experiments, we assess two different approaches when training the classification model: (a) fixing the weights, after pre-training the DAE, and (b) allowing fine-tuning of the entire classification network. Additionally, we apply two different strategies for embedding the DAE into the classification network: (1) by only importing the encoding layers, and (2) by inserting the complete autoencoder. Our best result was the combination of unsupervised feature learning through a DAE, followed by its full import into the classification network, and subsequent fine-tuning through supervised training, achieving an F1 score of 98.04% +/- 1.09 when identifying cancerous thyroid samples.
A Dynamic Logic for QASM Programs

We define a dynamic logic for QASM (Quantum Assembly) programming language, a language that requires the handling of quantum and probabilistic information. We provide a syntax and a model to this logic, providing a probabilistic semantics to the classical part. We exercise it with the quantum coin toss program.
Organization and Realization of Practical Teachings at the Faculty of Educational Sciences in Stip – Students’ Views

One of the prerequisites that must be fulfilled in order to create quality work skills is to provide quality teaching (with quality theoretical and practical knowledge) within educational institutions. Preparation of quality educational staff requires preparation of study programs with rich theoretical and practical knowledge. The paper explores the views and opinions of students - future kindergarten teachers, primary school teachers (from first until fifth grade) and pedagogues at the Faculty of Educational Sciences, Goce Delcev University in Stip, for the knowledge and skills they acquire during their practical classes. Students' views and opinions about the realization and quality of the practical teaching that they received, were perceived through the answers to the questions asked in the questionnaire which required them to determine the degree of knowledge gained, as well as the degree of satisfaction in the statements / questions offered. The results indicate that students are generally satisfied with the level of knowledge and experience that their professors transfer to them during the realization of pedagogical practice, but changes in the organization and realization of pedagogical practice may be introduced. It is important to emphasize the students' awareness of the importance and value of practical teaching and their positive attitude to greater attendance in kindergartens and schools, to monitor educational work and to become familiar with the organizational setting of these institutions. Students' views and opinions are an important voice, indicator and evaluator of the situation in higher education and they should be continuously consulted with one aim - to build quality study programs.
Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms

This paper proposes a distributed deep learning framework for privacy-preserving medical data training. In order to avoid patients' data leakage in medical platforms, the hidden layers in the deep learning framework are separated and where the first layer is kept in platform and others layers are kept in a centralized server. Whereas keeping the original patients' data in local platforms maintain their privacy, utilizing the server for subsequent layers improves learning performance by using all data from each platform during training.
PENERAPAN MODEL ARIMA DALAM PERAMALAN ANAK USIA 5–14 Th YANG TERINFEKSI HIV DI INDONESIA

Human Immunodeficiency Virus (HIV) is dangerous diseases for humans, and until now has not found a cure. Virus HIV is attacks the human immune system so that someone is susceptible to disease. This causes if someone is infected with HIV, then the person can experience an danger condition, it will even effect is death. In recent years, the number of children aged 5 – 14 years old that infected with HIV continues to increase. Therefore the author was moved to write about the application of the ARIMA model in forecasting the number of children aged 5 – 14 years old that infected with HIV in Indonesia by 2023. With the hope that the public or the govermment can find out the potential dangers of HIV disease, especially in children aged 5 – 14 years old. So that the public and govermment can jointly eradicate the spread of the HIV virus, especially in chidren. the result are obtained that the model that is suitable for use in forecasting is the ARIMA(0,1,2) models, with error value obtained is 0.057429. with the forecast value of the number of children aged 5 – 14 years old that infected with HIV in Indonesia from 2019 – 2023 in a row is : 570.82, 647.12, 734.14, 823.85, 944.83.
Path Planning of Mobile Robot Based on Neural Process-Particle Swarm Optimization

Aiming at the local optimal problem caused by precocious particles in the path planning process of mobile robot by traditional particle swarm optimization algorithm (PSO), a hybrid neural process-pso algorithm was constructed by integrating the idea of motion process prediction into PSO. The main idea is that in the next iteration of particle swarm individuals, the neural process is used to predict the individual location, increasing the diversity of particle swarm at the later stage of the iteration, and avoiding falling into local optimization too early, so as to improve the optimization ability of the algorithm. The improved algorithm is used to solve the robot path planning problem. The experimental results show that the proposed neural process-particle swarm optimization has better path planning ability and better comprehensive performance than the traditional PSO.
Analisis Kesalahan Bahasa Tataran Sintaksis Pada Proposal Karya Tulis Ilmiah

This research is backed by a problem that is often done by students who want to collect scientific proposals. The study focuses on mistakes in the interpretation of the syntax or sentences in the student writing proposal (LKTI). The purpose of this research is to uncover common mistakes and explain these mistakes so that students are concerned about writing effective sentences. The student writings analyzed in this journal are the Melodinesia boards as the learning Media of Indonesian culture-based mathematics in blind students can find out the most frequent mistakes in writing sentences by students. This type of research is a qualitative description. The results of the study were found to be a lot of sentences in the writing of student writings. The errors analyzed in this study focused on the syntax or sentence on the unsubjects aspect of the sentence; sentence arrangement error; excessive use of elements; Elimination of conjunction; Improper use of prepositions; An illogical sentence. From the analysis of the semantic landscape on the six aspects, the proposal of the paper still found many mistakes.
Trust Provisioning in the Transport Infrastructure

In any security process the role of trust anchors and trust roots have a complex interaction and applying them to the transport domain where many different forms of trust relationship have to work together is going to be one of greatest of the problems to surmount. ITS and CAV are data centric information systems in which the provenance of the data in the system is key to the success of the system. Provenance and integrity are underpinnings of trust, but in CAV and ITS there is no a priori knowledge of the source and value of data. This means that the provenance of a signal or message from the infrastructure is unlikely to be tested in advance and the verification of trust needs to be applied on demand for each message. Determining trust distribution in the transport infrastructure is one of the biggest unanswered questions regarding the viability of CAV.
CycLedger: A Scalable and Secure Parallel Protocol for Distributed Ledger via Sharding

Traditional public distributed ledgers have not been able to scale-out well and work efficiently. Sharding is deemed as a promising way to solve this problem. By partitioning all nodes into small committees and letting them work in parallel, we can significantly lower the amount of communication and computation, reduce the overhead on each node's storage, as well as enhance the throughput of the distributed ledger. Existing sharding-based protocols still suffer from several serious drawbacks. The first thing is that all non-faulty nodes must connect well with each other, which demands a huge number of communication channels in the network. Moreover, previous protocols have faced great loss in efficiency in the case where the honesty of each committee's leader is in question. At the same time, no explicit incentive is provided for nodes to actively participate in the protocol. We present CycLedger, a scalable and secure parallel protocol for distributed ledger via sharding. Our protocol selects a leader and a partial set for each committee, who are in charge of maintaining intra-shard consensus and communicating with other committees, to reduce the amortized complexity of communication, computation, and storage on all nodes. We introduce a novel semi-commitment scheme between committees and a recovery procedure to prevent the system from crashing even when leaders of committees are malicious. To add incentive for the network, we use the concept of reputation, which measures each node's trusty computing power. As nodes with a higher reputation receive more rewards, there is an encouragement for nodes with strong computing ability to work honestly to gain reputation. In this way, we strike out a new path to establish scalability, security, and incentive for the sharding-based distributed ledger.
A Hypersensitive Breast Cancer Detector

Early detection of breast cancer through screening mammography yields a 20-35% increase in survival rate; however, there are not enough radiologists to serve the growing population of women seeking screening mammography. Although commercial computer aided detection (CADe) software has been available to radiologists for decades, it has failed to improve the interpretation of full-field digital mammography (FFDM) images due to its low sensitivity over the spectrum of findings. In this work, we leverage a large set of FFDM images with loose bounding boxes of mammographically significant findings to train a deep learning detector with extreme sensitivity. Building upon work from the Hourglass architecture, we train a model that produces segmentation-like images with high spatial resolution, with the aim of producing 2D Gaussian blobs centered on ground-truth boxes. We replace the pixel-wise $L_2$ norm with a weak-supervision loss designed to achieve high sensitivity, asymmetrically penalizing false positives and false negatives while softening the noise of the loose bounding boxes by permitting a tolerance in misaligned predictions. The resulting system achieves a sensitivity for malignant findings of 0.99 with only 4.8 false positive markers per image. When utilized in a CADe system, this model could enable a novel workflow where radiologists can focus their attention with trust on only the locations proposed by the model, expediting the interpretation process and bringing attention to potential findings that could otherwise have been missed. Due to its nearly perfect sensitivity, the proposed detector can also be used as a high-performance proposal generator in two-stage detection systems.
Walkthrough and History of the Virtual Fields Method

This paper presents the history of the Virtual Fields Method, an identification method developed 30 years ago in order to identify constitutive parameters from full-field measurements. This method was initially proposed in the case of bending plate problems, but it has progressively been adapted to many other testing configurations. In this paper we look back at the first developments of the method and highlight the main problems that were progressively tackled, especially in terms of increasing complexity of the constitutive equations that were considered, and improvement of the identification technique itself to make it more efficient.
Per un ascolto efficiente del portoghese europeo. Percorsi dal basso e dall’alto

Abstract – This contribution offers a discussion on the enhancement of the top-down approach in the listening of oral texts in European Portuguese, by Italian-speaking learners. Portuguese has a vowel system that is particularly rich when compared to Italian. In addition, the vowels in non-accented syllables undergo a process of reduction that makes them difficult to perceive by the ear of an Italian speaker. In order to face such a difficulty of perception, recognition and comprehension of the oral text, it is necessary on one hand to consider the specific problems at all linguistic levels, following a bottom-up perspective and, on the other hand, to focus on the contribution that inference can make to strengthen the possibilities of understanding the message.
Revealing Messages in Graphic Novel The Hobbit

This study analyze the messages that imply and how the author sends the messages in graphic novel The Hobbit. The data in this study are in the form of utterances and pictures that refer to the moral values also direct and indirect messages. The design of this study is descriptive qualitative. The method that the researcher used is library research. The data of this research are in the form of pictures and utterances. The steps of data collection are reading graphic novel The Hobbit, finding the messages in the form of  utterances and pictures in every chapter in graphic novel The Hobbit, writing / noting down the utterances and pictures that imply a message. The steps of data analysis are doing data reduction by choosing the relevant messages, describing the messages found using Nurgiyantoro’s theory on messages (2007), describing how the author sends messages based on its types using Mahsuri’s theory (2015), concluding the data. From the data, the researcher found thirty four data for the messages. The messages are mostly about how to behave well and to have good atitude towards others. There are three data showing the messages are delivered in a direct way by the author, and there are thirty one data that shows indirect way.
Object Detection to Evaluate Image-to-Image Translation on Different Road Conditions

In vision-based navigation, image-to-image translation systems have been recently proposed to remove the effect of variable road conditions from images, in order to normalize the available data towards a common distribution (e.g. sunny condition). In this context, previous works have proposed image de-raining using generative adversarial networks (GAN) and evaluated performance based on pixel-to-pixel similarity measures that do not account for semantic content relevant for driving. Here, we propose to evaluate the performance of a de-raining GAN using an object detection neural network pretrained to find cars and pedestrians. We conducted experiments on the CARLA simulator to collect training and evaluation data under several weather conditions. Results indicate that GAN de-rained images achieve a high object detection performance in some conditions, but on average lower than object detection on the original rainy images. Future work will concentrate on improving semantic reconstruction and detection of other road elements (e.g. lanes, signs).
Penerapan Metode Ripple Down Rules Untuk Mendiagnosa Penyakit Burung

Birds or poultry are members of vertebrate animals that have feathers and wings. Diseased birds certainly look different from their normal condition and exhibit strange symptoms, if they are usually agile and active or often chirping, but when they are sick the bird looks limp, and chooses more silence. Of the symptoms that arise there are symptoms that can be seen by the eye or clinically and by looking at these symptoms can be known what diseases attack birds. To get a solution to the disease, a tool or system is needed to do it. The Ripple Down Rule (RDR) method is one method that has expert system inference / search capabilities and knowledge acquisition. By using the RDR method a system will be able to infer or identify several types of diseases suffered by birds as experts with clinical symptoms such as input. Recognized disease data adjusts rules (rules) that are made to be able to match the symptoms of bird disease stored in the system.
DeepRS: Deep-learning Based Network-Adaptive FEC for Real-Time Video Communications

This work proposes an innovative approach to handle packet loss in real-time video streaming scenarios in a more sophisticated way -- Predicting packet loss pattern on time field by deep learning model.
High dimensional expansion using zig-zag product

We wish to renew the discussion over recent combinatorial structures that are 3-uniform hypergraph expanders, viewing them in a more general perspective, shedding light on a previously unknown relation to the zig-zag product. We do so by introducing a new structure called triplet structure, that maintains the same local environment around each vertex. The structure is expected to yield, in some cases, a bounded family of hypergraph expanders whose 2-dimensional random walk converges. We have applied the results obtained here to several known constructions, obtaining a better expansion rate than previously known. Namely, we did so in the case of Conlon's construction and the $S=[1,1,0]$ construction by Chapman, Linal and Peled.
Bayesian Local Sampling-based Planning

Sampling-based planning is the predominant paradigm for motion planning in robotics. Most sampling-based planners use a global random sampling scheme to guarantee probabilistic completeness. However, most schemes are often inefficient as the samples drawn from the global proposal distribution, and do not exploit relevant local structures. Local sampling-based motion planners, on the other hand, take sequential decisions of random walks to samples valid trajectories in configuration space. However, current approaches do not adapt their strategies according to the success and failures of past samples. In this work, we introduce a local sampling-based motion planner with a Bayesian learning scheme for modelling an adaptive sampling proposal distribution. The proposal distribution is sequentially updated based on previous samples, consequently shaping it according to local obstacles and constraints in the configuration space. Thus, through learning from past observed outcomes, we maximise the likelihood of sampling in regions that have a higher probability to form trajectories within narrow passages. We provide the formulation of a sample-efficient distribution, along with theoretical foundation of sequentially updating this distribution. We demonstrate experimentally that by using a Bayesian proposal distribution, a solution is found faster, requiring fewer samples, and without any noticeable performance overhead.
SISTEM INFORMASI AKUNTANSI PERSEDIAAN BARANG BERBASIS KOMPUTER PADA BAGIAN GUDANG DI SEKOLAH CINTA KASIH JAKARTA

Technology development has a very important role in information management. Processing information related to large and fast inventory flows in a company certainly requires accuracy, efficiency, and effectiveness of information systems. This problem is experienced by the Sekolah Cinta Kasih in the activities of those who still use the manual recording system. The type of research used is descriptive research with a case study approach. By doing a series of system analysis, the author intends to design a computerized inventory information system. The system uses Microsoft Access 2010 which is capable of being operated in a Microsoft Windows XP operating system or a higher version of Windows.The computerized information system is expected to provide various facilities for the warehouse in processing database information and reports that are accurate, fast, and efficient. And minimize errors in inventory control. The new system design aims to prevent errors in the Accounting Information System Inventory by the Cinta Kasih School.
Optical Flow Analysis of Emotional Human Facial Expressions – Forensic Application

Emotional facial expressions have been suggested as indicators of a person’s intended actions when they display a set of specific facial reactions. Our aim was to investigate Optical Flow to identify specific emotions with the long-term objective to create an algorithm that would automate the process. This would remove all allegations of bias to give security personnel an objective way of screening individuals in a crowd who may require closer scrutiny. This paper describes our preliminary studies, using film clips to elicit dynamic natural facial responses. This has identified key elements that need to be evaluated further, such as gender and age of the people under observation, as well as specific facial elements that may be of greater benefit than whole facial images for the purpose of screening crowds for potential perpertrators of anti-social behaviour.
Pembelajaran 3D Printing sebagai metode perancangan produk kostum (Studi Kasus: industri cosplay)

Currently costume products can be enjoyed not only for film, drama or theater productions but for people as a form of appreciation to the fictional and non-fictional characters in two dimensions. Cosplay (abbreviation of a word: costume and play) in Indonesia has developed into a industry within a period of 15 year due customer needs. Even as cosplayer (people who cosplay) who has long been active in cosplaying, develop tecniques and the use of the material in process of designing cosplay costumes until introduce profession cosmaker (an abbreviation of costume maker). They have needed experience to cultivate material and technique that support skill of cosplayer and cosplayer craft’s to design costume for custom or mass, like 3D printing. This technique have been used to simplify the design process costume because efficiently and with more having the accuracy of finite dimensionnal produce of the successive visual better. For this reason, this research is needed with the aim that 3D printing can be a part of and enrich the design of costume products so that it can take part of basic in learning basic education design. This purpose will achieve through the qualitative study in a series of stage of the methodology grounded theory to declare that 3D printing can be a part in learning basic education design to answer the challenges the industrial revolution 4.0 through phenomenology approach to phenomena cosplay in Indonesia for 15 year who experienced by writer directly design cosplay costume. Key words: 3D printing, Indonesia cosplay industry, method of design, learning, costume product
Research and development of an automated video surveillance system to perform special functions.

A complex algorithm for creating an automated system for recording and displaying information from aircraft and observation in interactive operator control mode was presented. An architecture for encrypted transmission of video streaming from several cameras from an aircraft with in-flight video stabilization and projection of a virtual reality helmet on a 360-degree perspective was proposed.