Reviewerly/exHarmony huggingface dataset:
Understanding Natural Language Instructions for Self-Driving Cars and Grounding

Recently, practical applications of automatic driving have been rapidly developing. In the future, it must be necessary to enable interactive operation by natural language in order to easily operate autonomous cars. We therefore attempt to realize the correspondence relationship, i.e., a part of symbol grounding, between the driving instructions expressed in natural language and the objects in the real world recognized by the sensors equipped with a car, and then convert the driving instructions into the particular spatial meaning description to operate autonomous cars. In this study, we particularly focus on the parking operation of a car. We propose two methods: one is extracting spatial semantics from parking instructions, and the other is corresponding spatial semantics with the real-world environment. The structure trees given by Combinatory Categorial Grammar (CCG) are used as intermediate representation in exacting spatial semantics. If unknown words appear, we estimate them by using Conditional Random Field. In order to increase the accuracy of CCG parser, we implement a reranker of parse trees. These parse trees are converted into tree structures called Spatial Description Clause (SDC). We extend the framework of SDC by adding two new semantic categories, VIEW and STATE, so as to be able to ground more variety of the instructions for driving a car in the real-world environment. In corresponding spatial semantics with the real-world environment, we generate probability graphical models called Generalized Grounding Graph and output places or objects which correspond each word. The accuracy of all grounding among the sentences correctly parsed is 79.2%.
An Image Registration Framework for Discontinuous Mappings Along Cracks

A novel crack capable image registration framework is proposed. The approach is designed for registration problems suffering from cracks, gaps, or holes. The approach enables discontinuous transformation fields and also features an automatically computed crack indicator function and therefore does not require a pre-segmentation. The new approach is a generalization of the commonly used variational image registration approach. New contributions are an additional dissipation term in the overall energy, a proper balancing of different ingredients, and a joint optimization for both, the crack indicator function and the transformation. Results for histological serial sectioning of marmoset brain images demonstrate the potential of the approach and its superiority as compared to a standard registration.
SecureBP from Homomorphic Encryption

We present a secure backpropagation neural network training model (SecureBP), which allows a neural network to be trained while retaining the confidentiality of the training data, based on the homomorphic encryption scheme. We make two contributions. The first one is to introduce a method to find a more accurate and numerically stable polynomial approximation of functions in a certain interval. The second one is to find a strategy of refreshing ciphertext during training, which keeps the order of magnitude of noise at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mover accent="true"><mml:mi>O</mml:mi><mml:mo>˜</mml:mo></mml:mover><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:msup><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mn>33</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mfenced></mml:math>.
Autosoccer: An Automatic Soccer Live Broadcasting Generator

Low-cost live broadcasting is highly anticipated for soccer matches that involve heavy expenses in human and equipment currently. This demo showcases the autoSoccer system to approach the target. It takes a fix-view panoramic soccer video as the input and automatically generates its live broadcasting, which continuously focuses on the most interesting area in the soccer field. To this end, a novel pipeline is developed by leveraging both low-level motion and visual features, and high-level soccer-related semantics. By appropriately utilizing these clues, autoSoccer is capable of delivering a soccer match video analogous to human directed. Demo on school soccer show that autoSoccer produces videos with satisfactory watching experience. Moreover, it executes in real-time on a PC with Intel i7 CPU and one Nvidia Titan XP GPU.
Rancang Bangun Pemantau Daerah Endemik Malaria Berbasis IoT Menggunakan Metode Profile Matching

Abstract— Malaria is an endemic disease transmitted by Anopheles mosquitoes, which causes increased morbidity and mortality (morbidity and mortality), maternal and child health disorders, intelligence, labor force productivity, and detrimental to tourism. From these problems, a tool was developed to monitor endemic malaria areas in real-time by using temperature and humidity data for an area. Then analyzed using the profile matching method to calculate the weight/score gap in temperature and humidity conditions using interpolation, the data will be sent by the DHT 11 sensor found on NodeMcu. The application used to find out in real-time using the Blynk application as an internet of things board with a Wifi connection for the next step NodeMcu requires coding using the Arduino IDE program, for testing the location system used consists of 5 locations namely: L1 = Jl.Magelang, L2 = Jl.Maguwoharjo, L3 = Jl.Babarsari, L4 = Jl.Wates km9, L5 = Jl.Ali times km 8. Obtained temperature and humidity data (L1 = 36.30), (L2 = 18.32), (L3 = 23.45), (L4 = 38.33), (L5 = 35.53), then the gap weight calculation using the interpolation method obtained temperature and humidity data (L1 = 3,800; 3,974), (L2 = 4,600; 4,179), (L3 = 5,000; 5,000), (L4 = 3,400; 4,282), (L5 = 4,000; 5.00), the results obtained from rank 5 locations are malaria-endemic areas, rank 1 = L3-Babarsari, rank 2 = L5-Jl.Kaliurang km 8, rank 3 = L2-Jl.Maguwoharjo, rank 4 = L1-Jl.Magelang, rank 5 = L4-Jl.Wates. from the ranking data, the location must be prioritized L3-Babarsari. Keywords—: Malaria; DHT 11, NodeMCU; Profile Matching; Blynk.
USING INFORMATION SYSTEMS AND TECHNOLOGIES IN HIGHER EDUCATION INSTITUTIONS

The rapid development in technology has provided the globalization and to facilitate the exchange of information. The importance of tertiary education has been increasing due to its contribution for development plan’s manpower requirements in quality and quantity. Developments in information systems and technology have provided significant opportunities for higher education institutions. In this research; the implementation and development of information systems and technology in higher education institutions are chosen as a problem in comparison with global scale and taking into account the development of proposals. In this context; the developed countries’ higher education institutions’ were examined, after then universities’ information systems and technologies’ current status and degree of functionality are compared, and finally the proposals were developed. For this purpose; higher education institutions’ level of readiness for e-transformation, information technologies’ contribution to today's class and on the quality of education in shaping the contribution of higher education to shape the future and global competition ready workforce, the growing importance of information technologies on distance learning and on lifelong learning, higher education institutions’ role, and contribution to development of information technology, and the contribution of the envisaged objectives of the development plan.
Showing Your Work Doesn't Always Work

In natural language processing, a recently popular line of work explores how to best report the experimental results of neural networks. One exemplar publication, titled "Show Your Work: Improved Reporting of Experimental Results," advocates for reporting the expected validation effectiveness of the best-tuned model, with respect to the computational budget. In the present work, we critically examine this paper. As far as statistical generalizability is concerned, we find unspoken pitfalls and caveats with this approach. We analytically show that their estimator is biased and uses error-prone assumptions. We find that the estimator favors negative errors and yields poor bootstrapped confidence intervals. We derive an unbiased alternative and bolster our claims with empirical evidence from statistical simulation. Our codebase is at http://github.com/castorini/meanmax.
The Art of Coaching: Addressing Mobile Technology Integration in a Complex Hospital School Environment

This qualitative study examined the effects of a coaching program as part of a professional development effort to support integration of mobile technologies in a hospital school setting. The professional development consisted of two components: (a) the researchers introduced pedagogical models for incorporating mobile technology in education and (b) a technological expert (the iCoach) provided one-on-one and small-group personalized coaching. After studying the contextual challenges and teachers’ needs over a period of 12 months, an effective coaching model emerged. This was a personalized model geared to each teacher and tailored to the unique features of this hospital context. An integral part of the model was a reflection strategy, which encouraged teachers to question their teaching with the purposeful use of technology. Data collection involved teachers’ reflections, iCoach reflections, and semi-structured interviews. Many themes emerged from the data analysis; the two main ones were the uniqueness of the setting and the multiplicity of demands on the teachers and the iCoach. The study developed a set of guidelines to help teachers use technology in an integrated pedagogical way.
Sistem Pakar Untuk Mendeteksi Gangguan Pencernaan Dengan Metode Backward Chaining

Digestive disorders are various types of problems that occur in the body's digestive system, ranging from the mouth, esophagus, stomach, small intestine, large intestine, and anus. The digestive system functions to receive and digest food which is then converted into nutrients for the body to absorb. The absorbed nutrients will then be distributed throughout the body through the bloodstream. In addition, the digestive system is also tasked with separating and removing parts of food that are not needed and cannot be digested by the body. Digestive disorders that occur can be very difficult and should not be ignored. Many do not know that symptoms that are felt in the body are the result of certain digestive disorders, so it is often too late to be known and result in the need for a visit to the doctor. An Expert System is needed that can help the community in knowing the symptoms of the causes and prevention of digestive disorders in the body. Expert system to detect digestive disorders in this study uses backward chaining inference where the search process starts from the goal, which is the conclusion that becomes the solution to the problem at hand.
Efisiensi Penyisihan Kadar Amoniak Limbah Cair Rumah Sakit dengan Proses Adsorpsi Karbon Aktif Bijih Plastik

Latar Belakang: Tingginya kunjungan pasien akan meningkatkan kandungan amoniak limbah cair rumah sakit.Kadar amoniak yang tinggi menimbulkan pencemaran perairan. Adsorpsi karbon aktif merupakan teknologi alternatif dalam menurunkan kadar amoniak limbah cair.Tujuan penelitian ingin mengetahui pengaruh variasi waktu kontak karbon aktif bijih plastik terhadap penurunan kadar amoniak limbah cair rumah sakit.Metode: Jenis penelitian eksperimen dengan rancangan pretest-postest dengan kontrol. Populasi, seluruh air limbah yang diambil dari influent RSUD Al Ihsan Kabupaten Bandung, sedangkan sampel sebagian air limbah yang diambil dari populasi dengan teknik pengambilan gabungan waktu.Data yang telah terkumpul dianalisis menggunakan uji anova.Hasil: Hasil penelitian menunjukkan rata-rata persentase penurunan kadar amoniak untuk waktu kontak 60 menit 18,40%, 90 menit35,07 %, dan 120 menit 48,77 %. Hasil uji Anova diperoleh nilai p 0,001 lebih kecil dari 0,05 (α 5%).Simpulan:Terdapat pengaruh yang bermakna antara variasi waktu kontak karbon aktif bijih plastik terhadap penurunan kadar amoniak limbah cair. ABSTRACTTitle : Elimination Efficiency of Ammonia Content of Hospital Liquid Waste with Active Carbon Plastic Ore Adsorption.Background: Considerable visits from patients increases the content of ammonia within the liquid waste of the hospital which, in turn, results in water pollution. Adsorption of active carbon is an alternative technology in reducing the ammonia level of liquid waste. This research aims at revealing the contact time of active carbon plastic ore towards the degression of ammonia level of liquid waste. Method: This research was an experiment designed by pretest-posttest design with control. The population of the research was all the liquid waste taken from the influent of RSUD Al Ihsan Bandung, while the sample of the research was taken from the population by time combiningcollection technique. The collected data was then analyzed using anova. Result: The results of the research show that the average of the degression percentage for contact time of sixty minutes is 18.40%, 90 minutes 35.07%, and 120 minutes 48.77%. The value of t acquired from anova testing is 0.001, smaller than degression of ammonia level of liquid waste. Conclusion: There is a significant effect between the variation of contact time of plastic ore activated carbon to the decrease in the level of liquid waste ammonia.
Design Recommendation Information Architecture of Hospital Website Using Bottom-Up Approach on Card Sorting Method

&lt;p&gt;One of the public facilities that use websites is the hospital. However, there are some shortcomings complained by users related to the hospital website, which is the lack of information, confusing view of the menu, etc. Therefore, we need the information architecture to manage related content and website structure. The build of information architecture is done by using a bottom-up approach in card sorting. There are five stages in the research, which the first stage is a strategy to equalize the goal in the development of the hospital website either from the point of view of the organization or company and the user's point of view of the hospital website. The second stage is a scope to find out what are the needs of the user in order to build the hospital website. The third stage is structure, which is the formation of the initial structure of information architecture by using a bottom-up approach to the card sorting method. The type of card sorting that is commonly used is the open card sorting. The fourth stage is the skeleton stage, which is used to make information architecture model design using wireframe and prototype. The fifth stage is the test using findability testing and usability testing. From the test, the design of information architecture is made to fulfill the desires and needs of users, as proven by the results of findability testing achieved 15.9 seconds, and the results of usability testing achieved 80.33. Hence, by the results of these tests, it can be concluded that the design of information architecture at the hospital website has a good value from findability testing and usability testing.&lt;/p&gt;
Formation of Value-Oriented Attitude to Academic Work as Problem of Future Primary School Teachers’ Training

The aim of the study is to actualise in theory and practice the approaches to the issue of forming a value-oriented attitude to academic work with regard to developmental trends in the Russian education. The article specifies general understanding of a value-oriented attitude to academic work among future primary school teachers (Ural State Pedagogical University students) and determines whether they are ready to form such an attitude in their future pupils. Scientific novelty of the research lies in interpreting its key definition of a “value-oriented attitude to academic work” thought of as an integrative quality, which manifests in pupils’ accepting education process as such, understanding its social significance, having a cognitive motivation, being ready for an overcoming situation, having sophisticated reflection skills. The result of the data analysis of the students survey makes it possible to conclude that the students are at an insufficient level of practice-oriented readiness to form a value-oriented attitude to academic work, which is also connected to the professional training process of future teachers.
Trust-UBA: A Corpus for the Study of the Manifestation of Trust in Speech

This paper describes a novel protocol for collecting speech data from subjects induced to have different degrees of trust in the skills of a conversational agent. The protocol consists of an interactive session where the subject is asked to respond to a series of factual questions with the help of a virtual assistant. In order to induce subjects to either trust or distrust the agent's skills, they are first informed that it was previously rated by other users as being either good or bad; subsequently, the agent answers the subjects' questions consistently to its alleged abilities. All interactions are speech-based, with subjects and agents communicating verbally, which allows the recording of speech produced under different trust conditions. We collected a speech corpus in Argentine Spanish using this protocol, which we are currently using to study the feasibility of predicting the degree of trust from speech. We find clear evidence that the protocol effectively succeeded in influencing subjects into the desired mental state of either trusting or distrusting the agent's skills, and present preliminary results of a perceptual study of the degree of trust performed by expert listeners. The collected speech dataset will be made publicly available once ready.
FPGA Implementation of Post-Quantum DME Cryptosystem

The rapid development of quantum computing constitutes a significant threat to modern Public-Key Cryptography (PKC). The use of Shor's algorithm with potential powerful quantum computers could easily break the two most widely used public key cryptosystems, namely, RSA and Elliptic Curve Cryptography (ECC), based on integer factorization and discrete logarithm problems. For this reason, Post-Quantum Cryptography (PQC) based on alternative mathematical features has become a fundamental research topic due to its resistance against quantum computers. The National Institute of Standards and Technology (NIST) has even opened a call for proposals of quantum-resistant PKC algorithms in order to standardize one or more PQC algorithms. Cryptographic systems that appear to be extremely difficult to break with large quantum computers are hash -based cryptography, lattice -based cryptography, code -based cryptography, and multivariate -quadratic cryptography. Furthermore, efficient hardware implementations are highly required for these alternative quantum -resistant cryptosystems.
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks

It is known that the current graph neural networks (GNNs) are difficult to make themselves deep due to the problem known as over-smoothing. Multi-scale GNNs are a promising approach for mitigating the over-smoothing problem. However, there is little explanation of why it works empirically from the viewpoint of learning theory. In this study, we derive the optimization and generalization guarantees of transductive learning algorithms that include multi-scale GNNs. Using the boosting theory, we prove the convergence of the training error under weak learning-type conditions. By combining it with generalization gap bounds in terms of transductive Rademacher complexity, we show that a test error bound of a specific type of multi-scale GNNs that decreases corresponding to the number of node aggregations under some conditions. Our results offer theoretical explanations for the effectiveness of the multi-scale structure against the over-smoothing problem. We apply boosting algorithms to the training of multi-scale GNNs for real-world node prediction tasks. We confirm that its performance is comparable to existing GNNs, and the practical behaviors are consistent with theoretical observations. Code is available at https://github.com/delta2323/GB-GNN.
Bidirectionally Self-Normalizing Neural Networks

The problem of vanishing and exploding gradients has been a long-standing obstacle that hinders the effective training of neural networks.Despite various tricks and techniques that have been employed to alleviate the problem in practice, there still lacks satisfactory theories or provable solutions. In this paper, we address the problem from the perspective of high-dimensional probability theory. We provide a rigorous result that shows, under mild conditions, how the vanishing/exploding gradients problem disappears with high probability if the neural networks have sufficient width. Our main idea is to constrain both forward and backward signal propagation in a nonlinear neural network through a new class of activation functions, namely Gaussian-Poincar\'e normalized functions, and orthogonal weight matrices.Experiments on both synthetic and real-world data validate our theory and confirm its effectiveness on very deep neural networks when applied in practice.
How Does Momentum Help Frank Wolfe?

We unveil the connections between Frank Wolfe (FW) type algorithms and the momentum in Accelerated Gradient Methods (AGM). On the negative side, these connections illustrate why momentum is unlikely to be effective for FW type algorithms. The encouraging message behind this link, on the other hand, is that momentum is useful for FW on a class of problems. In particular, we prove that a momentum variant of FW, that we term accelerated Frank Wolfe (AFW), converges with a faster rate $\tilde{\cal O}(\frac{1}{k^2})$ on certain constraint sets despite the same ${\cal O}(\frac{1}{k})$ rate as FW on general cases. Given the possible acceleration of AFW at almost no extra cost, it is thus a competitive alternative to FW. Numerical experiments on benchmarked machine learning tasks further validate our theoretical findings.
PENGARUH TEKNIK SURVEY QUESTION READ REFLECT RECITE REVIEW (SQ4R) DAN TEKNIK SKEMA TERHADAP KEMAMPUAN MEMBACA PEMAHAMAN MATA PELAJARAN BAHASA INDONESIA PADA SISWA SD NEGERI KELAS III DI KECAMATAN KARANGANYAR DEMAK

The purpose of this study to determin e the effect of Question Survey Technique Read Reflect Recite Review (SQ4R) Technique and Scheme Technique to improving comprehension reading skills of Indonesian language Lesson . Th e research is Quasy Experiment with the design of Non Equivalent Control Group Design. The population in this study were third grade students of Govermant Elementary School of KaranganyarDemak. Sampling technique used purposive sampling. The sample size were 40 respondents in SDN Undaan Kidul I and 40 respondents in SDN Cangkring B. This research instrument use d a questionnaire. The data analysis technique used paired t test and Mann Whitney test. The results of the study get a score before the SQ4R action is 32.5% with the category of incomplete and 67.5% of the category of completion. Posttest stage gets a score of 87.92% with a good category (complete). In the Schema action get a score before the action 37.5% in the incomplete category and 62.5% in the complete category. In the posttest stage get a score of 80.75% with a good category (complete). The results of the Paired Samples T Test for SQ4R measures p value 0.004 and in the scheme (p 0.001) so that it shows the influence of Survey Question Read Reflect Recite Review (SQ4R) and Scheme techniques for increasing reading comprehension skills in Indonesian subjects. Mann Whitney analysis results obtained p 0,000, which shows the differences in the influence of Survey Question Read Reflect Recite Review (SQ4R) Techniques and Scheme Techniques on improving reading comprehension skills in Indonesian subjects.