Reviewerly/exHarmony huggingface dataset:
Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions

Humans can obtain the knowledge of novel visual concepts from language descriptions, and we thus use the few-shot image classification task to investigate whether a machine learning model can have this capability.Our proposed model, LIDE (Learning from Image and DEscription), has a text decoder to generate the descriptions and a text encoder to obtain the text representations of machine-or user-generated descriptions.We confirmed that LIDE with machine-generated descriptions outperformed baseline models.Moreover, the performance was improved further with high-quality usergenerated descriptions.The generated descriptions can be viewed as the explanations of the model's predictions, and we observed that such explanations were consistent with prediction results.We also investigated why the language description improved the few-shot image classification performance by comparing the image representations and the text representations in the feature spaces.
UISMiner: Mining UI suggestions from user reviews

GUI is an essential factor influencing users’ perception of an app. As the visual bridge between the user and the application, the quality of a GUI should be measured by the users’ subjective feelings. Therefore, to improve the UI, developers should pay attention to what users think and actively respond to their feedback. User reviews from different channels provide a way for developers to obtain users’ suggestions about the UI, and it could be of great benefit to improve the UI of their apps. Methods to mine different information (e.g., bug report, feature request) from reviews have been proposed in the previous works, however, suggestion information for supporting UI improvement has not gained much attention. In this paper, we propose UISMiner (UI Suggestion Miner), a novel approach to automatically mine the suggestions about the UI design from reviews. The application of UISMiner relies on natural language processing and machine learning classifier, which involves three main steps: 1) gaining reviews related to UI suggestion by training a classifier; 2) extracting UI suggestions from the obtained reviews by defining rules. 3) associating UI suggestions with the corresponding part in the UI. The experiment based on Google Play shows that UISMiner can effectively obtain reviews related to UI suggestions and extract such information from the gained reviews, the F-measure can reach 87.71% and 76.99% respectively. Furthermore, the survey on 10 developers shows that the information provided by us is useful for updating the UI of apps.
Effects of Wastewater use on Soil Physico-chemical Properties and Human Health Status

Freshwater scarcity with an increasing population is a debatable scenario at the present time. Change in rainfall pattern results in agriculture production demanding more water. To be concerned with both problems, irrigation wastewater use is a compatible strategy to overcome both conditions. Wastewater composition depends upon source type (domestic or industrial) and location (area or site) where does the water evolved. Wastewater composition varied from site to site and area to area due to location differentiation. Wastewater helps the farmers to increment in total available plant macronutrients, micronutrients, and replication of microbial community in soil. But wastewater use has some cons that they cause the soil physicochemical properties affection such as soil conductivity, water retention capacity, bulk density, organic carbon ratio and pH. Now, the critical concern is an accumulation of heavy metals in the soil-plant system. In addition, wastewater contains an excessive amount of many heavy metals (zinc, nical, lead and chromium), and if they present in the soil in excessive amounts, then they cause hazards to plants, animals and human life. In this articles discussion are made about the wastewater uses in future prospects for agriculture and how they are important in sustainable agriculture. Wastewater uses have pros and cons, but groundwater levels are decreasing at an alarming rate.
Dynamic Background Subtraction in Video Surveillance Using Color-Histogram and Fuzzy C-Means Algorithm with Cosine Similarity

The background subtraction is a leading technique adopted for detecting the moving objects in video surveillance systems. Various background subtraction models have been applied to tackle different challenges in many surveillance environments.&#x0D; In this paper, we propose a model of pixel-based color-histogram and Fuzzy C-means (FCM) to obtain the background model using cosine similarity (CS) to measure the closeness between the current pixel and the background model and eventually determine the background and foreground pixel according to a tuned threshold. The performance of this model is benchmarked on CDnet2014 dynamic scenes dataset using statistical metrics. The results show a better performance against the state-of the art background subtraction models.
AR-Supported Mind Palace for L2 Vocabulary Recall

MnemoRoom4U is an AR (Augmented Reality) tool that uses a memory-palace strategy for foreign-language training. A memory palace helps information recall with the aid of object association in visualizations of familiar spatial surroundings. In MnemoRoom4U, paper or digital flashcards are re-placed with virtual notes containing L1 words and their L2 translations that are placed on top of real physical objects inside a familiar environment, such as one’s room, home, office space, etc. The AR-supported notes aid associative memory by establishing a relationship between the physical objects in the user’s mind and the virtual lexis to be retained in L2. Learners first set up a path through their familiar environment, attaching virtual sticky notes—each containing a target word to be memorized together with its corresponding source-language translation—to real-life objects (e.g. furniture in their homes or offices). They then take the same path again, reviewing all the words, and finally carry out a retention test. MnemoRoom4U is a technological artifact designed for specific didactic purposes in the Unity game engine with the ARCore augmented-reality plug-in for Android. This work takes a Design-Science approach with phenomenological, exploratory underpinnings tracking back to the efficiency of spatial mnemonics previously reported quantitatively and combines it with AR technology to effect L2 vocabulary recall.
Measuring the Relative Importance of Fiscal and Monetary Policy in Stimulating Economic Growth in Yemen: An Econometrics Study Using Co-integration Approach

The study attempted to analyze and evaluate the performance of monetary and fiscal policy for the period 2000-2014 and its effectiveness in increasing economic growth using the descriptive analytical and econometrics approach. The study also attempted to measure the relative importance of fiscal and monetary policies and their relationship to economic growth using the co-integration method and the error correction model, which allows measuring the relative importance of fiscal and monetary policy and its relation to economic activity. Analysis of fiscal and monetary indicators showed that there were imbalances and distortions in financial and monetary performance that contributed to the weak effectiveness of fiscal and monetary policy in achieving economic stability and stimulating economic growth.&#x0D; Keywords: co-integration method, fiscal policy, monetary policy, economic growth, error correction model.
Implementation of Wireshark Application in Data Security Analysis on LMS Website

Data security issues are an important aspect of data and information communication over networks. In addition, it is also necessary to look at the security side of the software. In addition to software, computers have an internet protocol in the form of HTTP which is commonly used to access websites. On the LMS website, students have access to lecture materials, discussion forums with lecturers and access to assignments given by lecturers. Wireshark is used to analyze network protocols, can log all packets going through and display detailed data. The purpose of this study is to use a Wireshark application to sniff LMS and pinpoint vulnerabilities in the system. The results of the sniffing process carried out using Wireshark on an LMS that uses the HTTP protocol clearly indicate the absence of encryption and expose the risk of vulnerabilities to the system. Recommendations given to LMS are the use of HTTPS protocol, implementation of Multi Factor Authentication, website log monitoring and password management. Recommended password management are periodic password changes, standardization policies for the use of characters in passwords and password hashing. It is hoped that when the recommendations are implemented it will improve security on the LMS website and reduce risks in data communications
Compressed LSTM using balanced sparsity

Neural network models based on LSTM have been widely used in the fields of automatic speech recognition (ASR) and natural language processing (NLP). However, to obtain higher accuracy, the computational complexity and memory footprint of LSTM need to increase continually. In order to speed up LSTM inference, some previous works propose to compress the model with weight pruning. Row-Balanced Sparsity (RBS) is one of the excellent method because of balancing distribution of nonzero elements with negligible precision degradation. However, the compression ratio of the model is greatly limited due to its large index occupancy.A Share Index Row-Balanced Sparsity(SIRBS) compression method is presented in this paper, which shares the indices between rows in one row cluster. In this way, not only the nonzero weight distribution can be balanced with a very small cost of indices, but also decoding cost can be omitted. Compared with RBS, our overhead of indices can be reduced by 2x-8x. The performance and accuracy of our method is evaluated on the CPU using PyTorch-Kaldi toolkit. When the sparsity rate is 90%, compared with dense LSTM, our compression ratio and speedup are 6.7x-8.9x and 7.0x-9.9x respectively, and the accuracy only decreases by 0.2%-1.4%.
Detection of Fake Online Reviews using Semi-supervised and Supervised learning

Abstract: Nowadays, when somebody wants to make some decisions about a product or a service everyone goes with the reviews as it has become an essential part of decision making. When a customer wants to order a product on an e commerce website firstly everyone checks the review section in detail and further proceeds for decision making about the product. If the reviews posted were satisfactory for the customer he may order the product thus reviews become a reputed parameter for the businesses and companies and also a great source of information for the customers. Every customer thinks that the reviews he/she is seeing is authentic and any manipulation in that from any individuals or any rival companies which may lead to fake data which will be labeled as fake reviews. This type of attempt if not noticed may let us think about the gen-unity of the data. So these reviews are the most important parameter for the businesses and companies. There exist some groups or persons who make use of these reviews to forge customers for their own interest or damage their competitors reputation. In order to solve this problem we uses Machine learning techniques(Supervised and semi-supervised) to detect whether the given review is fake or not with high accuracy. Along with this objective we also focus on developing models which need less data to train.Since we can’t always be able to get labeled data we use semi-supervised machine learning to make use of unlabeled data.It is understandable our model should be capable of giving results in reasonably less time. .In this paper we proposed many classification algorithm like Support Vector Machine algorithm (SVM) , Random Forest algorithm (RF) and deep neural network
Distance-based approach to quantum coherence and nonclassicality

We provide a coherence-based approach to nonclassical behavior by means of distance measures. We develop a quantitative relation between coherence and nonclassicality quantifiers, which establish the nonclassicality as the maximum quantum-coherence achievable. We compute the coherence of several representative examples and discuss whether the theory may be extended to reference observables with continuous spectra.
Place of Public Libraries of Ukraine in the Modern Information Space

The aim of the article is to reveal the current areas of representation of domestic public libraries on the Internet and social networks in the context of digitalisation. The research methodology of the article was based on the use of the optimal set of general and special research methods. In particular, the broad scientific principles of systematisation and comprehensiveness of knowledge, unity of theory and practice were used. The use of analysis, synthesis and generalisation also contributed to the achievement of this goal. The study of the information resource of the sites of regional universal scientific libraries was carried out from the point of view of the complexity of presenting the content of its generation, focused primarily on the needs of communities. The scientific novelty of the article is that the need to strengthen the representation of domestic public libraries on the Internet and social networks is justified, which complicates their organisational structure and diversification of non-traditional methods and forms of work. Current areas of Internet representation are represented in the example of the K. A. Timiryazev Vinnytsia Oblast Universal Scientific Library and D. I. Chizhevsky Oblast Universal Scientific Library, on the e-pages of which the developer has the opportunity to share information on Facebook, Twitter, Instagram and Google. It contributes to more effective dissemination of messages, popularisation of the book collection itself, the attraction of more users, formation of its positive image and ultimately accumulation of reputational capital. Conclusions. It is argued that today public libraries of Ukraine are actively involved in the process of digitisation of their funds, present digital copies on their websites and advertise this resource on social networks, adapting it to mobile devices. Domestic public libraries have digitised vast amounts of their holdings; the work was carried out according to their programmes, regardless of the plans of other libraries. The author has found that today the top public libraries of the country are authoritative institutions and powerful information centres that form and maintain multiple and high-quality information resources. The modern user has access to the world’s leading libraries and their information resources via the Internet, which constitute the world information space.
Collective Nouns

Collective Nouns Jessica Jacobs (bio) The Hebrew (tayva), normally translated as ark,can mean container, box, and, also, word. א.When Noah was still just a man, not yet sailor and savior,God said, "Make yourself a word, for I have decided to silence all flesh." Scraping muscle from a hide, his wife crouched nearby, listening. Without argument or question, without a singlesignal of warning to neighbors or friends,her husband—that little wind-up toy, God's docileerrand boy—complied. He built the wordto spec: big enough to holdtwo of every creature,but too small for her mother, too small for her brother, no matter how she begged. ב.From planks of gopher wood smeared with pitch,Noah built the word and God shut them upin it. Water crushed down from the sky, fountained from the seas, dissolvingliving dust and breath to reefs of hushed mud. [End Page 36] And Noah:a silent man in a wooden word drifting the silenced world. ג.With an otter placid as a stole across his shoulders,instead of talking, he lived in his hands, picking nits; troughing food and water, always more water; tending,tending to every walking, creeping, winged thing, to all beingsbut her—never lying beside her, never tastingthe taste of sleep, his tonguewithered to a husk. The dark hold was mobbed with chitter, roar, and screech without restraint, and from outside, the ceaseless babbleof wood and rain. She was drowningin languages she couldn't speak, and he never offered hera word of comfort. ד.When the rains finally ended, Noah bound a rope to the rafters.Before the raven, before the doves, he lowered himselffrom the word's one window. A splash,and he leashed the rope to his ankle; leanedback and let his hands fall empty, let the flood embrace him. Grime sloughed from him into the wavesuntil the only animal he smelled was himself. Noah bobbed there, a beaming buoy, tethered to the word in which the futurefloated, where his wife, unseen—the new Eve, humanity'sunnamed mother—looked out from the window and watched as he gave himself to the killing waters; looked past him, trying not to think of the death and rot that brothed him. [End Page 37] ה.Is a man good, she wondered, who can construct a word large enough for only a chosen few? ו.And now, regardless of any covenantthat once rainbowed the sky, before the worldis again silenced—the water and weather already rising— what noun collective, what peaceful fleetlashed by syntax and spring linesinto a sentence of survival, what new words can we build to save us all? [End Page 38] Jessica Jacobs jessica jacobs is the author of the books Take Me with You, Wherever You're Going; Pelvis with Distance; and Write It! 100 Poetry Prompts to Inspire, coauthored with her wife Nickole Brown. Jessica is the chapbook editor for Beloit Poetry Journal. Four Way Books will publish her collection in conversation with the Book of Genesis in 2024.* Copyright © 2021 University of North Carolina Wilmington
PageRank: Graph Processing Using Dataflow to Rank Web Pages According to Importance

When a huge number of web pages appear, it is difficult for users to find useful information they need.There is a method to solve this problem.It can sort web pages according to their importance.This method is called PageRank in Spark.In this work, we introduced how PageRank works to rank web pages by importance, as well as the purpose and calculation formula of each part of the programming models.What is more, we also discussed the efficiency of PageRank by tackling different sizes of the graph and further applications of PageRank.The conclusion is that PageRank takes less extra time to process larger-size images than it takes to process smaller-size images.In other words, it is efficient for PageRank to tackle large graphs.So, PageRank can be widely used in many aspects.
Named Entity Recognition for Partially Annotated Datasets

The most common Named Entity Recognizers are usually sequence taggers trained on fully annotated corpora, i.e. the class of all words for all entities is known. Partially annotated corpora, i.e. some but not all entities of some types are annotated, are too noisy for training sequence taggers since the same entity may be annotated one time with its true type but not another time, misleading the tagger. Therefore, we are comparing three training strategies for partially annotated datasets and an approach to derive new datasets for new classes of entities from Wikipedia without time-consuming manual data annotation. In order to properly verify that our data acquisition and training approaches are plausible, we manually annotated test datasets for two new classes, namely food and drugs.
Improving Vulnerability Prediction of JavaScript Functions using Process Metrics

Due to the growing number of cyber attacks against computer systems, we need to pay special attention to the security of our software systems. In order to maximize the effectiveness, excluding the human component from this process would be a huge breakthrough. The first step towards this is to automatically recognize the vulnerable parts in our code. Researchers put a lot of effort into creating machine learning models that could determine if a given piece of code, or to be more precise, a selected function, contains any vulnerabilities or not. We aim at improving the existing models, building on previous results in predicting vulnerabilities at the level of functions in JavaScript code using the well-known static source code metrics. In this work, we propose to include several so-called process metrics (e.g., code churn, number of developers modifying a file, or the age of the changed source code) into the set of features, and examine how they affect the performance of the function-level JavaScript vulnerability prediction models. We can confirm that process metrics significantly improve the prediction power of such models. On average, we observed a 8.4% improvement in terms of F-measure (from 0.764 to 0.848), 3.5% improvement in terms of precision (from 0.953 to 0.988) and a 6.3% improvement in terms of recall (from 0.697 to 0.760).
Improving the Feasibility of DS-based Collision Avoidance Using Non-Linear Model Predictive Control

In this paper we present a novel strategy for reactive collision-free feasible motion planning for robotic manipulators operating inside an environment populated by moving obstacles. The proposed strategy embeds the Dynamical System (DS) based obstacle avoidance algorithm into a constrained non-linear optimization problem following the Model Predictive Control (MPC) approach. The solution of the problem allows the robot to avoid undesired collision with moving obstacles ensuring at the same time that its motion is feasible and does not overcome the designed constraints on velocity and acceleration. Simulations demonstrate that the introduction of the MPC prediction horizon helps the optimization solver in finding the solution leading to obstacle avoidance in situations where a non predictive implementation of the DS-based method would fail. Finally, the proposed strategy has been validated in an experimental work-cell using a Franka-Emika Panda robot.
Computer Vision: The Effectiveness of Deep Learning for Emotion Detection in Marketing Campaigns

As businesses move towards more customer-centric business models, marketing functions are becoming increasingly interested in gathering natural, unbiased feedback from customers.This has led to increased interest in computer vision studies into emotion recognition from facial features, for application in marketing contexts.This research study was conducted using the publicly-available Facial Emotion Recognition 2013 data-set, published on Kaggle.This article provides a comparative study of four deep learning algorithms for computer vision application in emotion recognition, namely, Convolution Neural Network (CNN), Multilayer Perceptron (MLP), Recurring Neural Network (RNN), Generative Adversarial Networks (GAN) and Long Short-Term Memory (LSTM) models.Comparisons between these models were done quantitatively using the metrics of accuracy, precision, recall and f1-score; as well and qualitatively by determining goodness-of-fit and learning rate from accuracy and loss curves.The results of the study show that the CNN, GAN and MLP models surpassed the data, and the LSTM model failed to learn at all.Only the RNN adequately learnt from the data.The RNN was found to exhibit a low learning rate, and the computational intensiveness of training the model resulted in a premature termination of the training process.However, the model still achieved a test accuracy of up to 72%, the highest of all models studied, and it is possible that this could be increased through further training.The RNN also had the best F1-score (0.70), precision (0.73) and recall (0.73) of all models studied.