Reviewerly/exHarmony huggingface dataset:
Cross-Modal Joint Embedding with Diverse Semantics

Textual-visual cross-modal retrieval has been an active research area in both computer vision and natural language processing communities. Most existing works learn a joint embedding model that maps raw text-image pairs onto a joint latent representation space in which the similarity between textual embeddings and visual embeddings can be computed and compared, without leveraging diverse semantics. This paper presents a general framework to study and evaluate the impact of diverse semantics extracted from the multi-modal input data on the quality and performance of joint embedding learning. We identify different ways that conventional textual features, such as TFIDF term frequency semantics and image category semantics, can be combined with neural features to further boost the efficiency of joint embedding learning. Experiments on the benchmark dataset Recipe1M demonstrates that existing representative cross-modal joint embedding approaches enhanced with diverse semantics in both raw inputs and joint embedding loss optimization can effectively boost their cross-modal retrieval performance.
The Musician’s Experience of Brass Instruments

This chapter gives a definition of ‘brass instruments’ (or ‘labrosones’) and briefly outlines their variety and development over several millennia. The musician’s interpretation of the brass playing experience is described in terms of the pitches of the readily playable notes, slotting, compass, intonation control (note bending), dynamic range, timbre and articulation. Related concepts such as instrument wrap, mouth (or blowing) pressure, air flow, resistance and projection are discussed. The chapter concludes by exploring the ways the musical qualities and playability of brass instruments are assessed by players and listeners, taking into consideration haptic feedback, cross-modal interference, multisensory integration and the McGurk effect.
A Semantic Network Encoder for Associated Fact Prediction

Semantic network is a network of concepts connected by semantic relations. It contains two forms of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">binary semantic network</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiplex semantic network</i> . The associated fact prediction is a link prediction task that aims to infer the implicitly connected facts by mining the high-level representation of the network. Previous methods for associated fact prediction put much emphasis on the topological feature of network but not utilize the information of semantic expression. This paper proposes a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sem</b> antic <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> etwork <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> ncoder ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SemNE</b> ), which learns a feature mapping function from the binary semantic networks and can be applied to the multiplex semantic networks in a pre-training manner. SemNE is a two-stage framework that contains an embedding encoder and a prediction decoder. It jointly models the semantic information and network topology to enrich the network representation. A word self-organization method based on the factual boundary is proposed to unify the topological feature and the semantic feature representations. Experimental results on binary semantic networks show that SemNE achieves the state-of-the-art results in associated fact prediction and experimental results on multiplex semantic networks show that SemNE is scalable and can effectively improve the performance of existing models.
The Drivers of Stock Market Growth in Pakistan: How Relevant is Irrelevant?

Pakistan’s stock market has experienced severe downside volatility after its classification in MSCI’s emerging market index in 2016. Global pandemic, political instability and capital limitations through money laundering control mechanisms have exposed the foundations of capital allocation to an escalating risk of default. The situation prerequisites a review of contentious relationship between drivers of economic growth and stock market expansion.&#x0D; This paper seeks to examine the relationship between non-financial and banal drivers of economic growth and their relative impact on stock market development in Pakistan, building on the fact that financial market development is a direct outcome of economic growth and development of the generic capabilities of production and capital formation through profit trails.&#x0D; GMM approach is used to analyze the contribution of capital formation and allocation through generic productivity and risk allocation mechanism. The paper discusses GDP growth, Gross fixed capital formation, Private credit and use of IMF credit as instrumental in determining stock market expansion. Originality of the research transpires from the fact that key outputs of real sector have been added to analyze the development of financial market which have long been ignored as extraneous for empirical analysis.
민주시민 양성을 위한 일제강점기 공주 지역사 수업

This article was intended to reconstruct Gongju''s local history from a democratic perspective with the aim of cultivating students'' civic capabilities. For a long time, history education focused on knowledge education to foster national and ethnic identity. In recent years, as a member of the nation or the state, it has become widely recognized that the universal values of mankind, such as human rights, peace, freedom, and equality, and active descriptions of democracy are needed as devices for the realization of them, not just emphasizing their duties as members of the nation or nation.As history education focused on fostering democratic citizens, the local community also received new attention. Through local education, which reviews a single history provided by the state from a local perspective, students can develop their ability to think on their own, and expand their perception of history by revealing the plurality of history. Furthermore, it discovers social problems from the perspective of individuals and various communities, and grows students into beings who strive to solve them. Community classes raise the possibility of democratic civic education in that they foster identity as community members and give them the power to read and solve social problems.Japanese colonial era is the time when the regulation of ethnic narratives is as strong as modern times when nationalism has been at work. The class was designed to examine how the Japanese colonial policy was specifically applied to the region and explore important events at the local level and the lives of the local people from a democratic perspective. It was intended to provide the experience of rationally deducing what the residents of Gongju were looking for and existed in their lives in Gongju, a colonial city built by the Japanese colonial government. Furthermore, the purpose of local classes was to imagine a desirable community. It was intended to include the formation of local communities by individuals as self-reliant beings with ideas and rights, not as human resources that are organized according to the needs of the state or governed and utilized on the principle of discrimination/excluding.
A Novel Design of Ergonomic Ablution Place at Mosques in Arar, Saudi Arabia

Currently, there are hardly any uniform design specifications for the construction of ablution places at mosques in Saudi Arabia. Designs of ablution places are usually copied from designs of ablution places in other mosques or based on individual perspectives. Based on the researchers’ anecdotal observations, most of these designs are nonergonomic, uncomfortable, and less user-friendly for ablution in either sitting or standing position, especially for the elderly. In such designs, water splashing is common because of inappropriate heights of platforms or seats, insufficient distances between seats and faucets, and improper shapes and depths of water drain. The purpose of the present study is to present the design of a novel ergonomic ablution place to ensure comfortable, safe, and hygienic conditions. The design is particularly related to the size and height of seat, height and distance of faucet from the seat, depth and slope of water drain, and height and width of the platform. The design was implemented at four mosques in Arar, Saudi Arabia. Modifications at ablution places were made according to the proposed design and were appreciated by the local community. The design was communicated to the Directorate of Mosques in Arar and agreed upon for implementation in other mosques.
Unforgettable User Defined Seed Phrase for Cryptocurrency Wallets

In this paper I have discussed a new method of enabling a cryptocurrency wallet user to define his own unforgettable seed phrase. An algorithm named SEEPT (Seed Phrase Transformation) is provided and illustrated with a real user defined seed phrase as input and a BIP39 standard seed phrase generated as output through cryptographic transformations. Discussed in detail is how an unforgettable seed phrase can be generatedfrom a set of names or words that are specific to a user’s personal life. Explained in detail as to how a wallet user can reconstruct his seed phrase on demand without depending on any storage, thereby relieving him from the burden of memorizing it frequently or storing it digitally on a computer hardware or physically on a paper or metal media.
Hybrid Transform-Based Image Compression Using Adaptive Grid Scanning

A lot of attention has been paid by the researchers for lossy color image compression. Due to the high uses of multimedia, a high image compression ratio while keeping good image quality is still a research issue. In this paper, an image compression is proposed using adaptive scanning, in which discrete wavelet transform (DWT) is performed to source image, discrete cosine transform (DCT) is performed to obtained DWT elements. Next, nonzero quantized discrete cosine transform (DCT) coefficients are represented in less number of bits. First, the source image is divided into 16 × 16 grids and then DWT transformed, later the obtained elements are divided into 8 × 8 grids and then DCT is performed, quantized, differential encoded, and an indexed vector is formed from the quantized matrix using adaptive scanning. Furthermore, the indexed vector is divided into two indexed vectors, namely \(nonzero\) vector and zerocount vector. The nonzero coefficients are stored in \(nonzero\) vector and the number of zeros preceding a nonzero coefficient is stored in zerocount vector. A new block is introduced to store the number of bits required to store the highest value of nonzero coefficient in \(nonzero\) vector so that all the nonzero elements can be represented in that many bits rather than a fixed number of bits. The experimental results show that the proposed method has achieved more compression ratio with slight reduction in image quality as compared to existing recent methods.
Performance Comparison of Imputation Methods in Building Energy Data Sets

Statistical procedures for missing data imputation techniques have vastly improved, yet selection and suitability of optimal imputation technique for particular application\datasets\context still confusing. This works frames the missing-data problem in building energy measurement systems, review different imputation methods and suggest the optimal imputation technique for missing values for energy metering data set. The main objective of this paper is to show performance of different imputation techniques with respect to accuracy and computation time in energy meter data. Missing values in the energy metering data set are imputed by seven imputation methods such as last value carried forward (LVCF), Mean, Median, Mode, multiple imputation by chain equation (MICE); K-nearest neighbors (K-NN) and long short term memory (LSTM). The performance of each imputation method is compared with respect to accuracy and execution time under a missing completely at random assumption. Based on the two evaluation criteria the LVCF imputation is very fast with high accuracy among single point imputation. The LSTM deserves the best among the seven imputation methods for energy metering data set, but the tradeoff is computation time compared to LVCF.
Development Project Management Information Systems at Unicode Indonesia

Unicode Indonesia is a company engaged in information technology, especially manufacturing multiplatform applications. The company needs a system that can increase employee performance for evaluation material. This need is supported by conditions where the employees send reports they do on that day. It will make a problem if more employees or projects are being managed. From the literature study, the project management apps like Microsoft Project, Oracle Primavera, and Trello cannot support the teaching process of project management course at most US universities and then decided to make internal software. The company's R&D (Research and Development) team is conducting technology research to build applications that can run on multiple platforms with one coding process. Based on research results, Quasar Framework is a solution at this time. This framework can make a great view in minutes. After the system is created, the system is tested using black-box testing. 5 respondents from the Unicode Indonesia Company via Google Form. The test results show that the system is feasible to use. Based on the test results, it can be concluded that the project management information system can help the company meet its needs.
A Switch Based Power Aware VM Consolidation Method for Cloud Datacenter

Ubiquitous access has made the cloud computing very popular. Due to its wide acceptability, size of cloud data center is increasing every day. The enormous size of the datacenter is posing a challenge to the cloud service providers in terms of huge electricity bills. It has its environmental impact too in terms of carbon footprint. Hence, managing the cloud resources in energy efficient way is the need of the hour. Researchers have proposed many energy efficient methods based upon the concept of switching off idle machines. There is very less focus on equally important network component i.e. switch. In the same direction we have proposed a Switch based Power Aware (SPA) VM consolidation method to minimize the energy consumption of the cloud data center by considering the utilization rate of physical machines as well as the switches of the cloud datacenter. The result of the proposed method shows a significant decrease in the energy consumption of the cloud datacenter.
Gaussian Mixture Model Based Semi-supervised Sparse Representation for Face Recognition

Sparse representation generally relies on supervised learning, however, the samples in real life are often unlabeled and sparse representation cannot make use of the information of the unlabeled samples. In this paper, we propose a Gaussian Mixture Model based Semi-supervised Sparse Representation (GSSR) for face recognition, and it takes full advantage of unlabeled samples to improve the performance of sparse representation. Firstly, we present a semi-supervised sparse representation, which is a linear additive model with rectification and makes all rectified samples conform to Gaussian distribution. Then, we reconstruct a new dictionary that derived from predicting the labels of unlabeled samples through Expectation-Maximization algorithm. Finally, we use the new dictionary embedded into sparse representation to recognize faces. Experiments on AR, LFW and PIE databases show that our method effectively improves the classification accuracy and has superiority even with a few unlabeled samples.
On Quasi Ordinal Diagram Systems

The purposes of this note are the following two; we first generalize Okada-Takeuti's well quasi ordinal diagram theory, utilizing the recent result of Dershowitz-Tzameret's version of tree embedding theorem with gap conditions. Second, we discuss possible use of such strong ordinal notation systems for the purpose of a typical traditional termination proof method for term rewriting systems, especially for second-order (pattern-matching-based) rewriting systems including a rewrite-theoretic version of Buchholz's hydra game.
An Effective Rotational Invariant Key-point Detector for Image Matching

Traditional detectors e.g. Harris, SIFT, SFOP... are known inflexible in different contexts as they solely target corners, blobs, junctions or other specific human-designed structures. To account for this inflexibility and additionally their unreliability under non-uniform lighting change, recently, a Sparse Coding based Key-point detector (SCK) relying on no human-designed structures and invariant to non-uniform illumination change was proposed. Yet, geometric transformations such as rotation are not considered in SCK. Thus, a novel Rotational Invariant SCK called RI-SCK is proposed in this paper. To make SCK rotational invariant, an effective use of multiple rotated versions of the original dictionary in the sparse coding step of SCK is proposed. A novel strength measure is also introduced for comparison of key-points across image pyramid levels if scale invariance is required. Experimental results on three public datasets have confirmed that significant gains in repeatability and matching score could be achieved by the proposed detector.
Penerapan Konsep Desa Literasi Berbasis Kearifan Lokal dalam Meningkatkan Minat Baca Masyarakat di Desa Lembang Lohe Kecamatan Kajang Kabupaten Bulukumba

The purpose of this research is to describe the application of the concept of Literacy Village in increasing the interest in reading the community in Lembang Lohe Village, Kajang District, Bulukumba Regency. This type of research is descriptive qualitative. Collecting data using interviews, observation, and documentation. Data analysis consists of data collection, data reduction, data presentation, and drawing conclusions. The results of this study explain that the application of the concept of Literacy Village 1) Literacy activities through counseling on the importance of education, recruitment and upgreading of facilitators, implementing the teaching process of the calistung method based on local wisdom, life skills assistance, routine activity programs for targeted communities and creating a village reading corner are well implemented, 2) 12 competent Facilitators who assist the community in eradicating illiteracy, 3) Catalysts as agents of change, 4) Provision of a Village Incubator, namely a reading corner with a cultural program for at least 5 minutes of reading and forming a smart house with the program 1 hamlet 1 smart house, 5 ) The application of teaching the Calistung method to the community was carried out using the pretest and post-test method, the results of the pretest of 60 selected learning citizens were illiterate, so with the method of facilitating literacy volunteer team facilitators for 2 times a week and continued with family implementing tutors, while the results l Post-test has increased success in recognizing letters, basic reading and arithmetic by up to 85%.
The Role of Geoinformatics in Renewable Energy Potential Estimation for Smart Cities - Emphasis on Solar and Wind Energy

Smart cities aim to enhance and sustain the quality of life for its residents. The focus in this research is to examine the potential location of constructing smart cities based on a major aspect of the availability and abundance of renewable energy that can be harvested to sustain such a city, with emphasis on wind and solar energy. Potential estimation for such renewable energy resources is investigated. Geoinformatics involves integrating and processing of geospatial data obtained from different earth measurement technologies, along with the capability of integrating attribute data (e.g. socio-economic). The ability of Geoinformatics to involve various data input in the process of decision-making has made Geoinformatics a viable approach for energy potential estimation. The role of Geoinformatics in estimating the potential of renewable energy is crucial and spatial analysis is utilized to build relations between different input parameters to reach for a potential production of energy at specific location.
Teacher and Technology in Foreign Language Education During the COVID-19 Pandemic: A Perspective from the Philosophy of Technology

During the COVID-19 Pandemic, online learning became a contingency plan for the abrupt school closure. A whole range of problems arose due to the lack of preparedness for "online learning for all". This paper focuses on the problems of online language teaching and learning and attempts to address the issues of the relationship between teacher and technology, and the potential of technology, by adopting new ideas from the philosophy of technology in recent years, such as "being rejected by technology" and "the mediating role of technology". Only through collaboration with technology can teachers become complete actants and agents in online teaching. In order to fully explore the potential of technology in online learning, the positive mediating role of technology needs to be kept in mind. Rather than the conventional theory of "technology as tool", "human-machine coupling" is a better attitude for us to adopt.
A Gas Turbine Virtual Reality Application Migration to Mixed Reality: Development Experience

This paper discusses several technical issues and difficulties faced while converting a Gas Turbine Virtual Reality (VR) application to a Mixed Reality (MR) application. Direct conversion from a VR to MR is feasible. However, it is found that several techniques or methods employed in VR are ineffective in MR. This is due to the Head Mounted Device used in the MR application allows users to see the real world while visualising computer-generated image, whereby the same function is not applicable in VR. Among the functions in VR which are not compatible with MR are teleporting and interaction. These issues were discovered during a prototype testing of the MR system. Based on the observation, it is found that in an MR system, the view inside HMD is overcrowded when a virtual background is visible along with the actual background. In addition to that, in an MR system, users prefer to physically walk towards the virtual object rather than teleporting using a controller. The discussion is not only highlighting the problem faced but also the solutions. In general, the objective of this paper is to share the technical issues experienced during the development process. It is expected that this knowledge can be helpful to future researchers or developers.
Research on Emotion Classification based on Complex Network and Ensemble Learning

Abstract Because the traditional feature extraction is based on the statistical information such as document frequency and word frequency, the selection of feature words is ignored, and the semantic correlation between words in the text is ignored. The feature selection method based on complex network takes into account the semantic association between words, but does not take into account the statistical information such as word frequency. The above methods are not satisfactory for the selection of feature words, which affects the effect of text classification. Therefore, this paper combines the two, proposes a new method for feature selection, and in order to solve the problem of low accuracy rate of single classification algorithm, USES integrated learning [1] to strengthen the classification algorithm. The results show that this method is feasible and achieves good classification effect.