Reviewerly/exHarmony huggingface dataset:
Labor training in design and technological activities of students

The aim of this article is to establish the effectiveness of labor training of students by using modern pedagogical and technological systems; determination of strong interest in implementation of project activities, as well as determination of teachers' attitude to implementation of labor training based on project-technological activities. The main method in the article is the experiment method; also, the questionnaire and observation methods were used. The result of the research is to establish the necessity of introducing design-technological activities into the process of labor education as a form of motivation and formation of strong interest of schoolchildren. It was found that project-technological activity is positively perceived by teachers in the field of labor education and is considered by them as effective and corrosive. It is stated that the labor lessons form a set of knowledge and skills in the technological sphere, develop a strong interest in creative work, and create conditions for the realization of intellectual abilities.
SGD with Low-Dimensional Gradients with Applications to Private and Distributed Learning

In this paper, we consider constrained optimization problems subject to a convex set C. Stochastic gradient descent (SGD) is a simple and popular stochastic optimization algorithm that has been the workhorse of machine learning for many years. We show a new and surprising fact about SGD, in that depending on the constraint set C, one can operate SGD with much lower-dimensional stochastic gradients without affecting its performance. In particular, we design an optimization algorithm that operates with the lower-dimensional (compressed) stochastic gradients, and establish that with the right set of parameters it has the exact same dimension-free convergence guarantees as that of regular SGD for popular convex and nonconvex optimization settings. We also present two applications of these bounds, one for improving the empirical risk minimization bounds in differentially private nonconvex optimization, and other for reducing communication costs with distributed SGD. Additionally, we also show that this connection between constraint set structure and gradient compression also extends beyond SGD to the conditional gradient (Frank-Wolfe) method. The geometry of the constraint set, captured by its Gaussian width, plays an important role in all our results.
Modeling Question Asking Using Neural Program Generation

Author(s): Wang, Ziyun; Lake, Brenden | Abstract: People ask questions that are far richer, more informative, and more creative than current AI systems. We propose a neuro-symbolic framework for modeling human question asking, which represents questions as formal programs and generates programs with an encoder-decoder based deep neural network. From extensive experiments using an information-search game, we show that our method can predict which questions humans are likely to ask in unconstrained settings. We also propose a novel grammar-based question generation framework trained with reinforcement learning, which is able to generate creative questions without supervised human data.
DESAIN KESIAPSIAGAAN TANGGAP DARURAT KEBAKARAN DI PASAR TRADISIONAL BOJA KABUPATEN KENDAL 2021

Boja Market is the main market in Boja District, Kendal, Central Java. Products from agriculture (fruits and vegetables) as well as industrial goods (clothing and electronics) can be sold in this market. Boja Market experienced a fire accident in 2007. As a result, traders suffered losses in the form of burned goods and trading activities in the market were hampered because traders lost their places to trade. Therefore, it is necessary to do fire prevention efforts to prevent fires from occurring. The purpose of this study was to design fire prevention efforts in the Boja Traditional Market, Kendal Regency based on the standard. This research is qualitative descriptive research. The research uses in-depth interviews and field observations, then the existing data is compared with standards for further analysis and design processes. The variable in this study is fire prevention efforts, includes: fire management and fire fighting facilities. The results of the study carried out a comparative analysis with applicable regulatory standards. The proposed design for supporting fire prevention efforts include: organizational structure for fire prevention, standard operating procedures for fire prevention and design of fire facilities layout plans. Traditional Boja Market, Kendal Regency, has carried out fire prevention efforts but has not been maximized so that researchers provide suggestions in the form of designs that are expected to be applied in the market.
DIGITAL BY DEFAULT

Most aspects of our everyday lives involve digital interfaces and corresponding customer experiences. Sandwiched between us and the rest of the world, they are our window to the world. This vantage point is often advantageous to those companies providing these interfaces to us, for they have access to the data and trust we place in their custody. Interfaces have always been valuable, even before the advent of digitisation. Owners of the interfaces between people are in a unique position to bridge the world in such a way that value flows reciprocally – and the smartest of interfaces know how to harvest this value. Harvesting value from any interface is of course a function of its ability to generate value to begin with. Interfaces are also always an intermediary; digital intermediaries have become very effective in connecting us with what we want and need. Their ability, like any interface, rests on their capacity to build trust and values so as to offer us a desirable mode for us to interact and transact with others. Traditionally, interfaces were, indeed, represented by actual faces, whilst nowadays we see a great deal of interfaces existing primarily or even purely in the digital world. Those trusted digital intermediaries have become very effective at connecting us for the exchange of value, sometimes even for high-value transactions – and doing so at a marginal cost to serve compared to that of actual people (i.e., human interfaces) who have made this their business. Other digital intermediaries prefer to augment human intermediaries, as is often the case in industries such as real estate or insurance…
Fast and Flexible: Human program induction in abstract reasoning tasks

Author(s): Johnson, Aysja; Vong, Wai Keen; Lake, Brenden; Gureckis, Todd M | Abstract: The Abstraction and Reasoning Corpus (ARC) is a collection program induction tasks that was recently proposed by Chollet (2019) as a measure of machine intelligence. Here, we report a preliminary set of results from a behavioral study of humans solving a subset of tasks from ARC (40 out of 1000). We found that humans were able to infer the underlying program and generate the correct test output for a novel test input example, with an average of 84% of tasks solved per participant, and with 65% of tasks being solved by more than 80% of participants. Additionally, we find interesting patterns of behavioral consistency and variability across the action sequences to generate their responses, the natural language descriptions used to describe the rule for each task, and the errors people make. Our findings suggest that people can quickly and reliably determine the relevant features and properties of a task to compose a correct solution, despite limited experience in this domain. This dataset offers useful insights for designing AI systems that can solve abstract reasoning tasks such as ARC with the fluidity of human intelligence.
Dependency Patterns of Complex Sentences and Semantic Disambiguation for Abstract Meaning Representation Parsing

Meaning Representation (AMR) is a sentence-level meaning representation based on predicate argument structure.One of the challenges we find in AMR parsing is to capture the structure of complex sentences which expresses the relation between predicates.Knowing the core part of the sentence structure in advance may be beneficial in such a task.In this paper, we present a list of dependency patterns for English complex sentence constructions designed for AMR parsing.With a dedicated pattern matcher, all occurrences of complex sentence constructions are retrieved from an input sentence.While some of the subordinators have semantic ambiguities, we deal with this problem through training classification models on data derived from AMR and Wikipedia corpus, establishing a new baseline for future works.The developed complex sentence patterns and the corresponding AMR descriptions will be made public1.
AGRICULTURAL ONTOLOGY VIRTUAL FUSION METHOD BASED ON MAPREDUCE

Agricultural knowledge has high importance because it can improve the efficiency of agricultural labor force and capital production significantly. Constructing agricultural ontologies can effectively help to utilize agricultural knowledge. The current agricultural ontologies mostly focus on the segmented domains of agriculture. The rate of sharing, reuse, and collaboration is relatively low. A method for agricultural ontology virtual fusion is proposed in this study based on MapReduce. The method was framed by MapReduce, the ontologies were stored in HBase, and virtual fusion was realized by requirement identification. In the map stage, it mainly conducts ontology community detection and ontology module virtual extraction. In the reduce stage, the ontology modules virtually extracted were mapped based on Tongyici Cilin (a Chinese synonym dictionary). The experiment selected a few ontologies about Chinese tea domain. The method was evaluated with respect to time, scale, and content. The experimental results show that the proposed method is feasible. Keywords: Agricultural ontology, ontology virtual fusion, ontology community detection, ontology module virtual extraction, ontology module mapping
Cybersecurity and Safety Measures

In recent years, the Cyber Security is most important challenge which plays most important role and must require in digital era where cybercrime become so frequent. Cyber security is all about to stop cyber-crime. Cyber security is must and we have to know about all safety measures required to stop cybercrime. This paper gives details information about cyber security and its safety measure. Also we will discuss about the activities related to it and how actually cybercrime happens and all steps taken by the various organization and Government to have cyber ethics everywhere. Cyber security provides protection against the cybercrime and teaches us what essential safety measures one need to follow from all cybercrimes. Securing online information is priority where everyone is involved with technology. Whenever anyone talked about cyber security, straight one thing comes in mind that is cybercrime and what safety measures need to take to be safe from it.
A Labeling Approach to Extraction out of Clausal Adjuncts

This paper accounts for the ban on extraction out of adjunct clauses, by relying on labeling, especially via feature sharing. Clausal adjuncts bear distinctive properties, when compared with arguments that have been widely researched on. One of the main characteristics of clausal adjuncts is their island-hood. Especially concentrating on the adjunct condition, which dictates that a clausal adjunct forms a strong island for extraction, a feature sharing-based approach is proposed in this paper. Departing from Truswell’s (2011) semantic approach, we propose that in cases where a clausal adjunct does not form a single event with a matrix clause vP, the merger of an adjunct is left as unlabeled. In other words, the unlabeled status of the merger formed by a clausal adjunct is a culprit for imposing the adjunct condition which bans further movement out of the clausal adjunct. Regarding the exceptional exemption from the adjunct condition, extraction out of a phrasal adjunct is exceptionally allowed in certain structural environment where the phrasal adjunct (with its internal structure being vP) is merged with the corresponding matrix clause vP to form a single event via theta-identification, which successfully results in labeling of the merger.
SIMPLIFICATION IN PHONOLOGICAL ACQUISITION (A CASE STUDY OF ATALA)

&lt;p&gt;This study aims to determine a child’s language acquisition at the phonological level and to describe its simplification of sounds. This research is a case study applying a qualitative descriptive approach. The subject observed is Atala, a boy aged 28 months. The data are obtained by recording and direct observation approximately one month. The data are transcribed and then identified by comparing the child's pronunciation with adult mode in order to see the simplification of the sounds in the child's speech. Based on the Ingram’s theory, 34 data presented shows the simplifications of sounds occur in the form of substitution consisting of stopping (6), fronting (9), and gliding (5); assimilation containing consonant harmony (3) and vowel harmony (2); while syllable structure consisting of last consonant reduction (2) and initial onset reduction (7). It is found that the simplifications of sounds commonly happen on the substitution covering fronting and stopping then syllable structure covering initial onset reduction. It is due to an indication that the child is imperfect to actively move the articulators in appropriate place or manner of articulation especially in producing some fairly complex sounds. There are some sounds cannot be produced in this case, such as sounds [r], [s], and [g], the subject obviously simplifies these sounds when he has to pronounce them in any positions of a word. Meanwhile he has been able to produce singly vowels and consonant [a], [i], [u], [e], [o], [p], [b], [t], [d], [m], [n], [ɲ], [ŋ], [w], [y], [j], [c], [l], [h], [?], but sometimes simplifies them in certain position of the word.&lt;/p&gt;
Application of LM and GDX algorithms in ANN modeling to Estimate the Groundwater Level Fluctuations

In the present investigation prediction of groundwater (GW) level near Varanasi is done by Artificial Neural Network (ANN) model. ANN is one of the methods used for development of the prediction model based upon the working of the human brain. This study gives a perfect prediction trained with two training algorithms LM (Levenberg-Marquardt) and GDX (Adaptive Learning rate with back Propagation). Data of eight wells, Annual precipitation, maximum and minimum Temperature, Relative humidity are the parameters taken as input, while future groundwater levels as output. The model proficiency and precision were estimated dependent on the R (regression coefficient) and RMSE (root mean square error) values approach. Observed R and RMSE values for most of the wells were tending towards unity in the LM approach. This LM approach is useful when we are short in terms of data, and it is expected that this strategy will give a genuinely exact outcome for a long span of data under examination. The LM method is discovered to be appropriate for determining any forecast of water fluctuations when there is a limitation in data constraint. This technique also gives an accurate result when the river is taken as an input in the artificial neural network (ANN) and the other input parameters.
The Effect of The POW + C-SPACE Strategy on Writing Skills of Students with Specific Learning Difficulties Attending Classes in the Resource Room

In this study, the POW + C-SPACE (Pick my idea - Organise my notes - Write and say more + Characters – Setting – Purpose – Action – Conclusion - Emotions) strategy shaped on the basis of the Self-Regulated Strategy Development Model (SRSDM) was presented through teachers working in the resource room, to evaluate the effects on story-writing skills of the students with Specific Learning Difficulties (SLD). The study was designed with the multiple probe model with probe phase between subjects. The independent variable of the research is the POW + C-SPACE strategy presented through the teachers working in the resource room. The dependent variable is the story writing levels of the children with SLD who are educated in the resource room. The study group consists of three teachers and their students with SLD in the resource room. The POW + C-SPACE strategy was presented to the students with SLD through the teachers working in the resource room. As a result of the study, it was concluded that when the teachers in the resource room presented the POW + C-SPACE strategy, it effectively developed the students' story writing skills.
재무 보고서의 키워드 검출 기반 딥러닝 감성분석 기법

Recent advances in artificial intelligence have allowed for easier sentiment analysis (e.g. positive or negative forecast) of documents such as a finance reports. In this paper, we investigate a method to apply text mining techniques to extract in the financial report using deep learning, and propose an accounting model for the effects of sentiment values in financial information. For sentiment analysis with keyword detection in the financial report, we suggest the input layer with extracted keywords, hidden layers by learned weights, and the output layer in terms of sentiment scores. Our approaches can help more effective strategy for potential investors as a professional guideline using sentiment values.
A neural network model of referent identification in the inter-modal preference looking task

Author(s): Duta, Mihaela; Plunkett, Kim | Abstract: We present a neural network model of referent identification in a preferential looking task. The inputs are visual representations of pairs of objects concurrent with unfolding sequences of phonemes identifying the target object. The model is trained to output the semantic representation of the target object and to suppress the semantic representation of the distractor object. Referent identification is achieved in the model based only on bottom-up processing. The training set uses a lexicon of 200 words and their visual and semantic referents, reported by parents as typically known by toddlers. The phonological, visual and semantic representations are derived from real corpora. The model successfully replicates experimental evidence that phonological, perceptual and categorical relationships between target and distractor modulate the temporal pattern of visual attention. In particular, the network captures early effects of phonological similarity, followed by later effects of semantic similarity on referent identification.
Analysis of sentimental images using deep learning approach

Deep learning also known as universal learning approach is a kind of machine learning used to carry out classification tasks straightforwardly from Medias like images, text or sound. The paper centers on execution examination of three pre-trained deep learning network with an end goal of classification of images which are related to sentimental analysis. The pre-defined convolutional neural networks (CNN) handled are AlexNet, ResNet50 and VGG16 with different Epoch. These networks are pre-trained on Twitter dataset. We focus on the structure of feelings deduced by our model and contrast it with what has been proposed in the psychology literature, and confirm our model on a bunch of pictures that have been utilized in psychology studies.  At long last, our work likewise gives a helpful instrument to the developing scholarly investigation of pictures consists of both photographs and memes on social networks. The network architectures are analyzed dependent on different means including, accuracy, precision, recall and F1-score. As per the experiment, out of three networks AlexNet gives better outcome as far as precision when compared to other networks.
Application of Expert System for Quality Evaluation in Higher Educational Institutes

The study examined impact of expert system as a tool for efficient teaching and learning process in educational system in Nigeria, using University of Port Harcourt in Rivers State, Nigeria as a case study. The specific objectives of the study include to: Identify types of expert systems available to the teaching and learning process in the study area; Identify expert systems used in the education sector in the study area; Identify human systems used in the study area; determine the extent to which expert systems influenced performance of students in the study area; determine the extent to which human expert system influenced performance of students in the study area; determine perception/satisfaction of teachers and students on application of expert systems in the teaching and learning process.
Employee Engagement and Organizational Change Initiatives: Does Transformational Leadership, Valence, and Trust Make a Difference?

The research investigates the role of transformational leadership in enhancing employee engagement during organizational change, and the individual and sequential mediating role of valence and trust in leadership. Based on cross-sectional data from a survey of employees in Bangladesh’s banking sector, the study uses structural equation modeling to test four hypotheses related to employee engagement and organizational change. The results showed that transformational leadership positively associated with employee engagement and that both valence and trust in leadership individually and sequentially mediate the relationship between transformational leadership and employee engagement. The research contributes to the areas of organizational change management and human resource management by clarifying the impact of transformational leadership on employee engagement. It also provides practical guidance on improving employee engagement in Bangladesh’s banking sector, and elsewhere, during periods of organizational change.
Volitional non-malicious insider threats: At the intersection of COVID-19, WFH and cloud-facilitated shadow-apps

In 2020, business operations were changed around the world due to disruptions of COVID-19, forcing organizations to migrate their workforce to a new Work From Home (nWFH) model quickly, and with limited planning, design or testing. As a result, new data security threats have emerged. This study aims to examine an insider-related threat to organizational data due to a self-support model of computing facilitated and accelerated by the mainstream proliferation of convenient, affordable and easily accessible cloud-based applications. Shadow Information Technology is a “double edged sword”-on the one end it boasts benefits of improved job performance, productivity, and cost savings, while on the other end it poses security threats. Moreover, with COVID-19 and nWFH, risks abound as established formal structures of the traditional workplace which reinforces data security policies and procedures are diminished. This gives employees more leeway to leverage unapproved apps with unvetted risk profiles for work-related tasks with the risk of introducing vulnerabilities into the work environment. We examine this issue through the lens of Theory of Planned Behavior (TPB) and General Strain Theory (GST), looking at it from the volitional non-malicious Insider Threat perspective. Study context is nWFH model against the backdrop of COVID-related strains. Expected contribution is in the areas of Insider Threats, Data Security, Shadow IT and Cloud Security. From a theoretical standpoint, the study aims to incorporate GST with the TPB in an attempt to increase its explanatory power while attempting to remedy one of its stated limitations. © AMCIS 2021.