Reviewerly/exHarmony huggingface dataset:
Knowledge Transfer with Weighted Adversarial Network for Cold-Start Store Site Recommendation

Store site recommendation aims to predict the value of the store at candidate locations and then recommend the optimal location to the company for placing a new brick-and-mortar store. Most existing studies focus on learning machine learning or deep learning models based on large-scale training data of existing chain stores in the same city. However, the expansion of chain enterprises in new cities suffers from data scarcity issues, and these models do not work in the new city where no chain store has been placed (i.e., cold-start problem). In this article, we propose a unified approach for cold-start store site recommendation, Weighted Adversarial Network with Transferability weighting scheme (WANT), to transfer knowledge learned from a data-rich source city to a target city with no labeled data. In particular, to promote positive transfer, we develop a discriminator to diminish distribution discrepancy between source city and target city with different data distributions, which plays the minimax game with the feature extractor to learn transferable representations across cities by adversarial learning. In addition, to further reduce the risk of negative transfer, we design a transferability weighting scheme to quantify the transferability of examples in source city and reweight the contribution of relevant source examples to transfer useful knowledge. We validate WANT using a real-world dataset, and experimental results demonstrate the effectiveness of our proposed model over several state-of-the-art baseline models.
The Interplay of Task Success and Dialogue Quality: An in-depth Evaluation in Task-Oriented Visual Dialogues

When training a model on referential dialogue guessing games, the best model is usually chosen based on its task success.We show that in the popular end-to-end approach, this choice prevents the model from learning to generate linguistically richer dialogues, since the acquisition of language proficiency takes longer than learning the guessing task.By comparing models playing different games (Guess-What, GuessWhich, and Mutual Friends), we show that this discrepancy is model-and taskagnostic.We investigate whether and when better language quality could lead to higher task success.We show that in GuessWhat, models could increase their accuracy if they learn to ground, encode, and decode also words that do not occur frequently in the training set.
Making Use of Latent Space in Language GANs for Generating Diverse Text without Pre-training

Generating diverse texts is an important factor for unsupervised text generation.One approach is to produce the diversity of texts conditioned by the sampled latent code.Although several generative adversarial networks (GANs) have been proposed thus far, these models still suffer from mode-collapsing if the models are not pre-trained.In this paper, we propose a GAN model that aims to improve the approach to generating diverse texts conditioned by the latent space.The generator of our model uses Gumbel-Softmax distribution for the word sampling process.To ensure that the text is generated conditioned upon the sampled latent code, reconstruction loss is introduced in our objective function.The discriminator of our model iteratively inspects incomplete partial texts and learns to distinguish whether they are real or fake by using the standard GAN objective function.Experimental results using the COCO Image Captions dataset show that, although our model is not pre-trained, the performance of our model is quite competitive with the existing baseline models, which requires pre-training.
The Implementation of Inclusive Education Program for Early Childhood

It is understood and stated by the law that every child has a right to access education.It is even more important for young children to enroll in early childhood education programs to get stimulation as early as possible for their growth and development.The inclusive program became the answer for children whose access was limited to special need school.The purpose of this article is to describe the implementation of inclusive education programs in early childhood (early childhood education programs).The research was conducted at two kindergartens in Singaraja, Bali, between January-March 2020.This research used a qualitative approach to assist with the study case method.The data collection techniques used were interview, observation, and documentation.Data analysis techniques of the research used interactive models by Miles and Huberman.The result showed that ( 1) not all teachers are familiar with the concept of inclusion; (2) however teachers showed a positive attitude toward students with special need; (3) the learning which designed based on the need of special children were limited; (4) teachers need support to enhance their skill to do early detection and create the appropriate learning and approach for children with special need in their class; (6) the facility to support special need children in school is very limited.
THE ASSIGNMENT OF TASKS TO THE NODES OF THE IOT DISTRIBUTED SYSTEM BASED ON REINFORCEMENT MACHINE LEARNING

The article describes issues of applying an adaptive approach based on reinforcement learning for assignment of the computing tasks to nodes of distributed Internet of Things (IoT) platform. The IoT platform consists of heterogeneous elements that are computing nodes. Classical approaches, methods, and algorithms for distributed and parallel systems are not suitable for task assignment in IoT systems due to its characteristics. The reinforcement learning method allows you to solve the problem of building a distributed system due to the adaptive formation of a sequence of computational nodes and the corresponding computational tasks. Thus, the article represents a method that makes IoT nodes capable of execution computing tasks, especially, which were previously designed for classical distributed and parallel systems.
Video Stream Session Migration Method Using Deep Reinforcement Learning in Cloud Computing Environment

In the resource scheduling of streaming Media Edge Cloud (MEC), in order to balance the cost and load of migration, this paper proposes a video stream session migration method based on deep reinforcement learning in cloud computing environment. First, combined with the current popular OpenFlow technology, a novel MEC architecture is designed, which separates streaming media service processing in application layer from forwarding path optimization in network layer. Second, taking the state information of the system as the attribute feature, the session migration is calculated, and gradient reinforcement learning is combined with in-depth learning and deterministic strategy for video stream session migration to solve the user request access problem. The experimental results show that the method has a better request access effect, can effectively improve the request acceptance rate, and can reduce the migration cost, while shortening the running time.
SINr: Fast Computing of Sparse Interpretable Node Representations is not a Sin!

While graph embedding aims at learning low-dimensional representations of nodes encompassing the graph topology, word embedding focus on learning word vectors that encode semantic properties of the vocabulary. The first finds applications on tasks such as link prediction and node classification while the latter is systematically considered in natural language processing. Most of the time, graph and word embeddings are considered on their own as distinct tasks. However, word co-occurrence matrices, widely used to extract word embeddings, can be seen as graphs. Furthermore, most network embedding techniques rely either on a word embedding methodology (Word2vec) or on matrix factorization, also widely used for word embedding. These methods are usually computationally expensive, parameter dependant and the dimensions of the embedding space are not interpretable. To circumvent these issues, we introduce the Lower Dimension Bipartite Graphs Framework (LDBGF) which takes advantage of the fact that all graphs can be described as bipartite graphs, even in the case of textual data. This underlying bipartite structure may be explicit, like in coauthor networks. However, with LDBGF, we focus on uncovering latent bipartite structures, lying for instance in social or word co-occurrence networks, and especially such structures providing conciser and interpretable representations of the graph at hand. We further propose SINr, an efficient implementation of the LDBGF approach that extracts Sparse Interpretable Node Representations using community structure to approximate the underlying bipartite structure. In the case of graph embedding, our near-linear time method is the fastest of our benchmark, parameter-free and provides state-of-the-art results on the classical link prediction task. We also show that low-dimensional vectors can be derived from SINr using singular value decomposition. In the case of word embedding, our approach proves to be very efficient considering the classical similarity evaluation.
Unsupervised Subcategory Domain Adaptive Network for 3D Object Detection in LiDAR

Three-dimensional object detection based on the LiDAR point cloud plays an important role in autonomous driving. The point cloud distribution of the object varies greatly at different distances, observation angles, and occlusion levels. Besides, different types of LiDARs have different settings of projection angles, thus producing an entirely different point cloud distribution. Pre-trained models on the dataset with annotations may degrade on other datasets. In this paper, we propose a method for object detection using an unsupervised adaptive network, which does not require additional annotation data of the target domain. Our object detection adaptive network consists of a general object detection network, a global feature adaptation network, and a special subcategory instance adaptation network. We divide the source domain data into different subcategories and use a multi-label discriminator to assign labels dynamically to the target domain data. We evaluated our approach on the KITTI object benchmark and proved that the proposed unsupervised adaptive method could achieve a remarkable improvement in the adaptation capabilities.
A Compact Quantum Reversible Circuit for Simplified-DES and Applying Grover Attack

Security of symmetric cryptosystem in post quantum era can be evaluated by implementing the algorithm on quantum platform. The number of qubits and ancilla bits used to design the quantum circuit is questionable as it impacts the cost of the circuit. This study proposes a compact and efficient quantum circuit for simplified DES which utilizes the lowest number of qubits and ancilla bits. The key generation steps are fused so as to maintain the essence of the algorithm and at the same time make the circuit compact. Key and plaintext is mixed without the help of ancilla bits. The results show that nineteen qubits and seven ancilla bits along with polynomial number of quantum gates are sufficient to implement simplified DES on quantum circuit. Quantum attacks are conducted on the proposed circuit using Grover's algorithm for key search as it acts as the black box for quantum search.
Zero-shot Cross-lingual Content Filtering: Offensive Language and Hate Speech Detection

We present a system for zero-shot cross-lingual offensive language and hate speech classification. The system was trained on English datasets and tested on a task of detecting hate speech and offensive social media content in a number of languages without any additional training. Experiments show an impressive ability of both models to generalize from English to other languages. There is however an expected gap in performance between the tested cross-lingual models and the monolingual models. The best performing model (offensive content classifier) is available online as a REST API.
Evaluation of Disleksia Belajar Mobile App for Assisting Dyslexic Junior School Students to Learn the Malay Language

Mobile apps are fast emerging as assistive learning platforms for pre-schoolers, as well as junior school students. In fact, not just the parents but also the junior-level teachers encourage children to use mobile learning apps due to its many benefits such as interactive, enjoyable and informal, etc. Needless to say, the advent of mobile apps must also equitable for students with learning difficulties to take advantage of the same learning opportunities as other students. Students with learning difficulties suffer from disabilities in language, information processing, etc. that prevent them from performing their academic tasks in the same manner as other students. It is crucial to realize the development of mobile learning apps for students with learning difficulties requires inclusive designs that make the apps usable for them. The paper presents an evaluation of a mobile appDisleksia Belajar, which developed for students with dyslexia in junior school to learn the Malay language. Dyslexia is a common learning difficulty that causes problems with language processing e.g. reading and writing. The evaluation is performed using SUS and Fun ToolKit (v3) techniques, which intend to explore the usability and engagement of the mobile app. There is a total of 12 students with dyslexia aged 7 to 12 years old recruited as the participants. The findings contribute towards understanding and improvement for the future development of mobile learning apps for students that having similar difficulty.
Eye movement prediction of individuals while reading based on deep neural networks

Traditional eye movement models are based on psychological assumptions and empirical data; thus, they cannot predict eye movement for previously unseen text and cannot predict individual differences while reading. This paper presents an eye movement model based on conventional psychology-based eye movement models using a bi-directional long short-term memory-conditional random field (bi-LSTM-CRF) neural network instead of empirical data sets. The model was trained to predict the eye movements of a user reading a previously unseen text based on the eye movements recorded for this person reading other texts as training data. Tests demonstrate that the model can achieve similar prediction accuracy than current machine learning models while requiring fewer features, which makes this model attractive for a range of real-time human-computer applications. 摘要 传统眼动模型基于心理学假设和经验数据构建, 不能对未见文本数据进行预测, 且不能解决阅读个体化差异问题。针对这一问题, 该文提出了一种利用深度神经网络预测读者注视点的眼动模型。与传统基于心理学的眼动模型不同, 该模型不是基于经验数据集, 而是基于双向长短期记忆-条件随机场 (bi-directional long short-term memory-conditional random field, bi-LSTM-CRF) 神经网络。该模型使用阅读过程中读者的眼球运动数据作为训练数据, 来预测该读者阅读其他文本时的注视点。计算机模拟结果表明：bi-LSTM-CRF模型能够使用较少的数据特征获得与现有机器学习模型相似的预测准确度, 这使所提出的模型在实时人机交互应用领域具有吸引力。
Coordinate Constructions in English Enhanced Universal Dependencies: Analysis and Computational Modeling

In this paper, we address the representation of coordinate constructions in Enhanced Universal Dependencies (UD), where relevant dependency links are propagated from conjunction heads to other conjuncts.English treebanks for enhanced UD have been created from gold basic dependencies using a heuristic rule-based converter, which propagates only core arguments.With the aim of determining which set of links should be propagated from a semantic perspective, we create a large-scale dataset of manually edited syntax graphs.We identify several systematic errors in the original data, and propose to also propagate adjuncts.We observe high inter-annotator agreement for this semantic annotation task.Using our new manually verified dataset, we perform the first principled comparison of rule-based and (partially novel) machine-learning based methods for conjunction propagation for English.We show that learning propagation rules is more effective than hand-designing heuristic rules.When using automatic parses, our neural graph-parser based edge predictor outperforms the currently predominant pipelines using a basic-layer tree parser plus converters.
Gerture controlled Robot

Gesture Controlled Car is a robot which can be controlled by simple human gestures. The user just needs to wear a gesture device in which a sensor is included. The sensor will record the movement of hand in a specific direction which will result in the motion of the robot in the respective directions. The robot and the Gesture instrument are connected wirelessly through radio waves. User can interact with the robot in a more friendly way due to the wireless communication. We can control the car using accelerometer sensors connected to a hand glove. The sensors are intended to replace the remote control that is generally used to run the car. It will allow user to control the forward, backward, leftward and rightward movements, while using the same accelerometer sensor to control the throttle of the car. Movement of car is controlled by the differential mechanism. The mechanism involves the rotation of both forth & rear wheels of left or right side to move in the anticlockwise direction and the other pair to rotate in the clockwise direction which makes the car to rotate about its own axis without any kind of forward or backward motion. The main advantage of this mechanism is the car with this mechanism can take sharp turn without any difficulty. The design and implementation of a gesture control robotic arm using flex sensor is proposed. The robotic arm is designed in such a way that it consists of four movable fingers, each with three linkages, an opposing thumb, a rotating wrist and an elbow. The robotic arm is made to imitate the human hand movements using a hand glove.
Research on the Trust Relationship of Jilin Province Agricultural Products Supply Chain Enterprises Based on the Theory of Green Environmental Protection Block chain

Abstract The successful advancement of the Internet + agriculture strategy in Jilin Province has accelerated the circulation efficiency of agricultural products in Jilin Province, and played a vital role in increasing the income of farmers and encouraging their enthusiasm for planting. Under the background of green environmental protection and low-carbon economy, the block chain technology solves the problems of inefficient information transmission in the supply chain led by core enterprises, low matching of information traceability requirements and capacity status, and endless information islands. Combined with block chain technology, analysis there are problems in the information optimization of the agricultural product supply chain in Jilin Province. The “block chain + agricultural product supply chain” is used to realize the model of decentralization, information sharing, and in-depth cooperation mechanism, so as to realize the coordination of the agricultural product supply chain and effectively improve the agricultural product The value-added process of the supply chain has established a three-party trust model for the government, core enterprises and consumers in the process of building a green agricultural supply chain.
Framework for Measuring Relevancy in Discovery Environments

Discovery environments are ubiquitous in academic libraries but studying their effectiveness and use in an academic environment has mostly centered around user satisfaction, experience, and task analysis. This study aims to create a quantitative, reproducible framework to test the relevancy of results and the overall success of Washington State University’s discovery environment (Primo by Ex Libris). Within this framework, the authors use bibliographic citations from student research papers submitted as part of a required university class as the proxy for relevancy. In the context of this study, the researchers created a testing model that includes: (1) a process to produce machine-generated keywords from a corpus of research papers to compare against a set of human-created keywords, (2) a machine process to query a discovery environment to produce search result lists to compare against citation lists, and (3) four metrics to measure the comparative success of different search strategies and the relevancy of the results. This framework is used to move beyond a sentiment or task-based analysis to measure if materials cited in student papers appear in the results list of a production discovery environment. While this initial test of the framework produced fewer matches between researcher-generated search results and student bibliography sources than expected, the authors note that faceted searches represent a greater success rate when compared to open-ended searches. Future work will include comparative (A/B) testing of commonly deployed discovery layer configurations and limiters to measure the impact of local decisions on discovery layer efficacy as well as noting where in the results list a citation match occurs.
A deep neural network approach for detecting wrong-way driving incidents on highway roads

One of the numerous drawbacks of existing systems for wrong-way driver detection (WWD) is that they require installation and maintenance of expensive sensor networks. More importantly, they fail to leverage on the growing number of traffic surveillance camera networks. Approaching wrong way driver detection from a computer vision standpoint is a rather intricate one if not well thought out. As such, recent methods which explored alternative deep learning approach for solving this problem have been shown to exhibit a high rate of false detection and consider very limited settings e.g. exit ramps. In this paper, we propose a more sophisticated computer vision framework to address the shortcomings of existing systems while also leveraging on existing preinstalled large-scale camera infrastructure to achieve real-time WWD detection with high precision. The proposed framework combines four modules working collaboratively to deliver desired results. This includes: (i) a Flow Detection Module which is initialized to determine the correct direction of flow by momentarily observing the traffic; (ii) a state-of-the-art object detection algorithm, in this case YOLOv5, for detecting all objects of interest from each frame; (iii) a sophisticated centroid-based object tracker coupled with Hungarian matching algorithm for efficiently tracking objects of interest; and (iv) a wrong way flagging module to flag vehicles moving opposite to a lane’s computed flow direction as they enter and exit the camera’s field of view. The Hungarian algorithm ensures that each object of interest is assigned a unique ID which not only reinforces tracking efficiency of the object tracker, but also provides traffic count capability. Tracking paths are compared against computed direction of flow to instantly detect wrong way driving. The proposed architecture achieves state-of-the-art performance with high True Positive Rate and low false detections. One of the several benefits of the proposed method is that it could potentially be integrated into the department of transport (DOT) surveillance system to significantly reduce the cognitive load pressure on traffic control agents who are overwhelmed by the large number of video feeds they are tasked to monitor in real-time. Alerts generated from this system could help mitigate such issues.
The Effect of Uniform Data Quantization on GMM-based Clustering by Means of EM Algorithm

In this paper, unlike the commonly considered theoretical scenario in clustering, wherein data attributes are accurately presented, we research how successful clustering can be indeed performed in situations wherein before being grouped the data have to be quantized, that is, represented with smaller accuracy. This describes situation wherein before being grouped the data are compressed due to their transmission through the bandwidth-limited channel. We consider scenario wherein the data modeled with the one-dimensional two-component Gaussian mixture model (GMM) are uniformly quantized and, after that, grouped by using the expectation-maximization (EM) algorithm. We study how the number of quantization levels and the quantizer support limits influence on the results of clustering.