Reviewerly/exHarmony huggingface dataset:
A Three-Valued Semantics for Typed Logic Programming.

Types in logic programming have focused on conservative approximations of program semantics by regular types, on one hand, and on type systems based on a prescriptive semantics defined for typed programs, on the other. In this paper, we define a new semantics for logic programming, where programs evaluate to true, false, and to a new semantic value called wrong, corresponding to a run-time type error. We then have a type language with a separated semantics of types. Finally, we define a type system for logic programming and prove that it is semantically sound with respect to a semantic relation between programs and types where, if a program has a type, then its semantics is not wrong. Our work follows Milner's approach for typed functional languages where the semantics of programs is independent from the semantic of types, and the type system is proved to be sound with respect to a relation between both semantics.
A Rule-Based System for Explainable Donor-Patient Matching in Liver Transplantation.

In this paper we present web-liver, a rule-based system for decision support in the medical domain, focusing on its application in a liver transplantation unit for implementing policies for donor-patient matching. The rule-based system is built on top of an interpreter for logic programs with partial functions, called lppf, that extends the paradigm of Answer Set Programming (ASP) adding two main features: (1) the inclusion of partial functions and (2) the computation of causal explanations for the obtained solutions. The final goal of web-liver is assisting the medical experts in the design of new donor-patient matching policies that take into account not only the patient severity but also the transplantation utility. As an example, we illustrate the tool behaviour with a set of rules that implement the utility index called SOFT.
Towards Gradually Typed Capabilities in the Pi-Calculus.

Gradual typing is an approach to integrating static and dynamic typing within the same language, and puts the programmer in control of which regions of code are type checked at compile-time and which are type checked at run-time. In this paper, we focus on the pi-calculus equipped with types for the modeling of input-output capabilities of channels. We present our preliminary work towards a gradually typed version of this calculus. We present a type system, a cast insertion procedure that automatically inserts run-time checks, and an operational semantics of a pi-calculus that handles casts on channels. Although we do not claim any theoretical results on our formulations, we demonstrate our calculus with an example and discuss our future plans.
An Oscillator Based Energy Efficient Computing Architecture for Smart Sensors

In the past decade, there is a fast-growing need for sensing technology applications. However, the implementation of sensing and proceeding creates a large burden on data processing. The idea for designing stable cluster state for coupled phase oscillator in pattern recognition is receiving significant attention. This paper gives an overview of an oscillator neural network (ONN) based hierarchical associative memory (AM) architecture using oscillator synchronization and stable cluster state for pattern recognition and how such architecture can be efficiently used in local processing units. The ONN based AM architecture can be easily achieved using CMOS technology on local hardware units. Unlike cloud computing system, our architecture provides an approach when it is under an offline mode which couldn't get responses from the web server. The proposed architecture can help perform sensing and data processing efficiently on the local device without connecting to the Internet.
Integrating RBF-based Neural Network Face Expression Recognition in Access System

Biometric recognition system such as facial recognition system was widely developed over the past few years. Facial recognition system is commonly used in security system to allow user to protect their privilege. The normal security like key or password is no longer relevant as people prefer an easier and flexible way. Therefore, this paper presents a better and easier way of security system that can recognize the user successfully and give the matching percentage. By using Radial Basis Function Neural Network in MATLAB, a face recognition system can be created. The RBF system will be trained by data as reference, input image will undergo the same process and the data obtained will be used to match with the data in the RBF to obtain the matching percentage. A suitable matching percentage reference was chosen from this analysis as the minimum require matching to access the security system where error rate is one of the main concerns where it is the unwanted result that might occur. Different threshold number, spread value, and sizes of dimension also tested, the differences on the output matching result were observed. By using the microcontroller to control a relay to control the magnetic door lock, the system was able to successfully control the door lock.
Managing Research and Data for Reproducibility and Transparency

Presentation to the 2019 workshop, Methods for Promoting Open Science in Social Policy sponsored by the Office of Planning, Research and Evaluation. Office of the Administration for Children and Families, U.S. Department of Health and Human Services, http://opremethodsmeeting.org/meetings/2019/.
LibGuides: Academic Resources for Teens: Home

A guide designed for teens to understand how to navigate the database Explora and how to use the information to their advantage
FANTrack: 3D Multi-Object Tracking with Feature Association Network.

We propose a data-driven approach to online multi-object tracking (MOT) that uses a convolutional neural network (CNN) for data association in a tracking-by-detection framework. The problem of multi-target tracking aims to assign noisy detections to a-priori unknown and time-varying number of tracked objects across a sequence of frames. A majority of the existing solutions focus on either tediously designing cost functions or formulating the task of data association as a complex optimization problem that can be solved effectively. Instead, we exploit the power of deep learning to formulate the data association problem as inference in a CNN. To this end, we propose to learn a similarity function that combines cues from both image and spatial features of objects. Our solution learns to perform global assignments in 3D purely from data, handles noisy detections and varying number of targets, and is easy to train. We evaluate our approach on the challenging KITTI dataset and show competitive results. Our code is available at https://git.uwaterloo.ca/wise-lab/fantrack.
RNS to Decimal Converter and Sign Detector Using Scaling for an RNS 3- Moduli Set {2n-1, 2n, 2n+1}

This paper covers the backward conversion process from residue or remainder number to decimal number with its sign detection whether positive or negative by using residue modulo set{2n-1,2n,2n+1} The paper proposes Residue or remainder number to decimal number converter and sign detector for 3-moduli set based on adder circuit and scaling technique by avoiding calculation of multiplicative inverse. This moduli set is mostly utilized in modulo residue or remainder number system oriented arithmetic calculations. Backward process of Residue or remainder number to decimal conversion and sign detection are very significant in comparison of magnitude and other applications. Though the concept of Chinese reminder theorem is mainly used for the reverse conversion and sign detection process, scaling technique is used to reduce dynamic size and avoid complex arithmetic manipulation. This paper attracts to low area and high speed scaling based effectuation and it is compared to conventional technique.
Daya Persuasif Dalam Wacana Kampanye Pilpres Sebagai Bahan Ajar Bahasa Indonesia di Tingkat SMP

The purpose of this study is to describe (1) analyzing the use of banners on the 2019-2024 Presidential Election sphere. (2) describes relevance of persuasive discourse on yhe presidential election banner for the 2019-2024 period in the learning of Junior High School level. The method used is the method of covering the basic techniques and advanced techniques. The basic techniques of the agih method is called the BUL technique (For Direct Elements) and advanced techniques which include marking techniques, dressing techniques. The results of this study indicate the existence (1) Diction consists of the use of abstract word, common words, and synonyms. (2) Variation of sentences which are divided into free sentence statements can be exchanged, objects as part of the predicate cannot be separated, sentences vary active-passive, varied sentences of news-commands-questions, and persuasive discourse, cantaining suggestions and invitations. From the results of the study found persuasive sentences on Presidential and Vice Presidential election banners that have potential elements developed in learning in Junior High School grade VIII.
TABOO LANGUAGE ON PHYSICAL AND MENTAL LIMITATIONS IN THE PIDIE RAYA COMMUNITY

The purpose of this study is to describe types of taboo language with regard to physical and mental limitations in the Pidie Raya community, which includes Pidie Regency and Pidie Jaya Regency. This field research with a qualitative approach used a descriptive-analytical method. The data of this research were taboo languages in a spoken form related to physical and mental limitations in the Pidie Raya community. The data were obtained from 16 informants who settled in eight villages within the sub-districts of Muara Tiga, Padang Tiji, Trienggadeng and Meureudu, in the regency of Pidie and Pidie Jaya. The data were collected using structured interview techniques supported by inducement techniques. The data were analyzed qualitatively through several stages (a) selecting data, (b) describing the whole data, (c) grouping data based on their types, and (d) analyzing data followed by drawing conclusions. The results showed that the taboo languages related to physical limitation include a) inherited physical deficiencies/abnormalities, and (b) physical limitations due to illness. While taboo languages related to mental limitation includes (a) mental disorders, (b) weak-minded, and (c) psychological disorders. Some taboo words about one's physical and mental limitations in the Pidie Raya community can be expressed in the form of refinement or euphemism. This is intended to prevent social inequalities among the members of the community.
Análisis de la evolución de la jurisprudencia del Tribunal Europeo de Derechos Humanos sobre el discurso del odio

The European Court of Human Rights is the highest jurisdiction for the protection of the liberties recognized by the European Convention on Human Rights. The Court exercises its powers by combining two criteria: the universal nature of these rights and the margin of national appreciation. In the field of hate speech, the ECHR has had to analyze what are the limits of freedom of speech, generating a broad and diverse jurisprudence that, in many cases, has not been free from controversy.
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation.

In the last few years, convolutional neural networks (CNNs) have demonstrated increasing success at learning many computer vision tasks including dense estimation problems such as optical flow and stereo matching. However, the joint prediction of these tasks, called scene flow, has traditionally been tackled using slow classical methods based on primitive assumptions which fail to generalize. The work presented in this paper overcomes these drawbacks efficiently (in terms of speed and accuracy) by proposing PWOC-3D, a compact CNN architecture to predict scene flow from stereo image sequences in an end-to-end supervised setting. Further, large motion and occlusions are well-known problems in scene flow estimation. PWOC-3D employs specialized design decisions to explicitly model these challenges. In this regard, we propose a novel self-supervised strategy to predict occlusions from images (learned without any labeled occlusion data). Leveraging several such constructs, our network achieves competitive results on the KITTI benchmark and the challenging FlyingThings3D dataset. Especially on KITTI, PWOC-3D achieves the second place among end-to-end deep learning methods with 48 times fewer parameters than the top-performing method.
Study of timbral influence of mallets on the steelpan through spectral analysis

The steelpan is an instrument with unique acoustic properties that allow for a wide range of timbral possibilities influenced by the choice of actuator. It is increasingly common for composers and performers to experiment with mallets made from a variety of materials that produce highly differentiated timbres. Examples of mallet types include wood and aluminum shafts covered with rubber tips, chopsticks, dowel rods, and cardboard tubes. Understanding how mallets interact with the steelpan is an important aspect of performance practice. Aside from standard rubber tipped mallets, most other mallets types are homemade and these characterizations will inform mallet design and construction in order to achieve the desired timbral result. While there have been studies analyzing the modes of vibrations of steelpan notes, the interactions between mallets and timbre hasn’t yet been studied and characterized. The goal of this work is to measure and characterize the timbral influence of a wide variety of mallets on high voiced steelpans through low level audio analysis with a focus on spectral features. Audio recordings were made with more than a dozen different mallets and several steelpans of three different types: tenor, double seconds, and double tenors.
GRLA 2019

Graphs are the universal data structures for representing the relationships between interconnected objects. They are ubiquitous in a variety of disciplines and domains ranging from computer science, social science, economics, medicine, to bioinformatics. In Recent years, extensive studies have been conducted on the graph analysis techniques. One of the most fundamental challenges of analyzing graphs is effectively representing graphs, which largely determines the performance of many follow-up tasks. This workshop aims to provide a forum for industry and academia to discuss the latest progress on graph representation learning and their applications in different fields. We hope more advanced technologies can be proposed or inspired, and also we expect that the direction of graph representation learning can catch much more attention in both academic and industry.
Deep Learning-Based Histopathologic Assessment of Kidney Tissue

BACKGROUND: The development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolutional neural network for multiclass segmentation of digitized kidney tissue sections stained with periodic acid-Schiff (PAS). METHODS: We trained the network using multiclass annotations from 40 whole-slide images of stained kidney transplant biopsies and applied it to four independent data sets. We assessed multiclass segmentation performance by calculating Dice coefficients for ten tissue classes on ten transplant biopsies from the Radboud University Medical Center in Nijmegen, The Netherlands, and on ten transplant biopsies from an external center for validation. We also fully segmented 15 nephrectomy samples and calculated the network's glomerular detection rates and compared network-based measures with visually scored histologic components (Banff classification) in 82 kidney transplant biopsies. RESULTS: The weighted mean Dice coefficients of all classes were 0.80 and 0.84 in ten kidney transplant biopsies from the Radboud center and the external center, respectively. The best segmented class was in both data sets (Dice coefficients, 0.95 and 0.94, respectively), followed by tubuli combined and interstitium. The network detected 92.7% of all glomeruli in nephrectomy samples, with 10.4% false positives. In whole transplant biopsies, the mean intraclass correlation coefficient for glomerular counting performed by pathologists versus the network was 0.94. We found significant correlations between visually scored histologic components and network-based measures. CONCLUSIONS: This study presents the first convolutional neural network for multiclass segmentation of PAS-stained nephrectomy samples and transplant biopsies. Our network may have utility for quantitative studies involving kidney histopathology across centers and provide opportunities for deep learning applications in routine diagnostics.
Analisa dan Perancangan Sistem Monitoring Sales dengan Digital Signature Pada PT. Cipta Pusaka Utama

Utilization of technological development is carried out by PT. Cipta Pusaka Utama as a provider of goods / services. The company has a sales team to carry out marketing activities. The problems that are currently faced by the company are that the current sales activities are not controlled so that many salespeople are cheating and this can give a loss to the company, so that sales activities must be monitored so that sales work in accordance with the rules set by the company. Based on these conditions, a system that is able to monitor sales performance is made. This system is based on android and web with the RAD system development model. Data collection methods include literature studies, observations, and interviews. With this system, management of sales activities is better with the supporting features of digital signatures, and managerial parties can also obtain information on sales visit reporting, as well as assessing sales performance with Key Performance Indicator (KPI). Keywords : Information Systems, Sales, Monitoring, Web, Android.
Preceding Vehicle Tracking using Particle Filter on the basis of the Shadow Likelihood and the Vehicle Likelihood

The objective of this study is to reduce rear-end vehicle collisions caused by human errors. In order to achieve this objective, we studied a technique to detect preceding vehicles with image processing using a monocular in-vehicle camera and a computer. As an image characteristic for vehicle detection, we adopt the shadow underneath vehicle. Furthermore, in order to detect the shadow with high precision, we study shadow tracking. In this paper, we describe a technique for vehicle tracking on the basis of the shadow underneath vehicle.
Towards a more efficient use of computational budget in large-scale black-box optimization

Evolutionary algorithms are general purpose optimizers that have been shown effective in solving a variety of challenging optimization problems. In contrast to mathematical programming models, evolutionary algorithms do not require derivative information
A SURVEY OF GRAMMAR LEARNING STRATEGIES USED BY EFL LEARNERS IN INDONESIA

The objective of the study is to identify the language learning strategies used most by the learners in Intermediate English Grammar class. In order to accomplish the objective, this study used quantitative research in the form of survey study. The original questionnaire used in this study is from Oxford (1990), modified by Kemp (2007) and Bayou (2015) which consists of 32 statements. The respondents of this study consist of 119 students in total from three Intermediate English Grammar classes, 52 students from Intermediate English Grammar A and 23 students from Intermediate English Grammar B, and 44 students from Intermediate English Grammar C. The result of the study shows that social strategy is the most used strategy by IEG students, while the lowest strategy is memory strategy. The implication of this study is there is a possibility that the students of Intermediate English Grammar class have tendency to learn together with their friends.
Evaluating Free Rides in Visualizations of Sets

A consideration for any visualization technique is the added valueit brings over other representations of data. A free ride, a conceptintroduced by Shimojima, occurs in a visualization when it revealssome fact that must be inferred from an alternative representationfrom which the visualization was derived. This paper presents preliminaryresults from an empirical evaluation of free rides occurringin visualizations of sets as compared to textual representations. Byfocusing on Euler, linear and Venn diagrams, our results suggest thatmore research is needed to establish when free rides are cognitivelybeneficial.
An efficient Secure Electronic Mail System based on Elliptic Curve Certificateless Signcryption

Electronic Mail or E-mail is an important development in the communication world.Therefore, the email security and efficiency has become a critical issue.Most of the existing email systems use either S/MIME (Secure/Multipurpose Internet Mail Extensions) or PGP (Pretty Good Privacy) which depend on Public Key Infrastructure (PKI) or Identity-Based Cryptography (IBC) and use inefficient signature-thenencryption techniques.Each one of these techniques has its own drawbacks.Recently, Certificateless Cryptography (CLC) and Elliptic Curve (EC) based signcrption which combines both signature and encryption in logically one step are developed to overcome these drawbacks with efficient methods.In this paper, a CLC-EC-signcryption based secure E-mail system is proposed.To make the system more efficient, the encryption key is hidden in the transmitted ciphertext itself.The system is highly secure as it uses multi-factor authentication technique includes IP address, password and fingerprint for registration and login.It provides all the security services: confidentiality, integrity, authentication, non-repudiation and forward secrecy with high efficiency compared with other recently existing schemes.Also, it is optionally for the user to send his email in clear form or signcrypted.Finally, it is practically implemented by C# programming language and it can work on the real network system without changing in the existing network architecture.