Reviewerly/exHarmony huggingface dataset:
Algorithm for Detecting Polarity of Opinions in Laptop and Restaurant Domains

The easy access to the Internet and the large amounts of information produced on the Web, Artificial Intelligence and more specifically the Natural Language Processing (NLP) provide information extraction mechanisms. The information found on the Internet is presented in most cases in an unstructured way, and examples of this are the social networks, source of access to opinions, products or services that society generates daily in these sites. This information can be a source for the application of the NLP, which is responsible for the automatic detection of feelings expressed in the texts and its classification according to the polarity they have; it is the area of analysis of feelings, also called opinion mining. This paper presents a study for the detection of polarity in a set of user opinions issued to Restaurants in Spanish and English.
Analysis of the Design of Digital Video Security Monitoring System Based on Bee Population Optimization Algorithm

With the concept of “wireless city”, 3G, WIFI and other wireless network coverages have become more extensive. Data transmission rate has achieved a qualitative leap, providing feasibility for the implementation of mobile video surveillance solutions. The mobile video monitoring system based on the bee population optimization algorithm proposed in this paper makes up for the defects of traditional network video surveillance, and according to the video surveillance system monitoring command, the optimal visual effect of the current state of the observed object can be rendered quickly and steadily through the optimization of the camera linkage model and simulation analysis.
The Application of Machine Learning Methods in Drug Consumption Prediction

Nowadays, drug abuse is a universal phenomenon. It can bring a huge damage to the human body and cause an irreversible result. It is important to know what can lead to the abusing so that it can be prevented. In order to prevent the abusing of those drugs, it is necessary to figure out what elements make people abuse the drug and how they relative to the abusing. According to the drug consumption data from the UCI database, Big Five personality traits (NEO-FFI-R), sensation seeking, impulsivity, and demographic information are considered to be the relative elements of the abusing. However, how they affect on the abusing of drugs is not clear so they cannot predict the probability of a person whether he is going to abuse a drug. There are many traditional ways to analysis the data based on scoring, such as give every element a score and but they can only tell an inaccurate predictive value. Machine learning is very hot nowadays because of its strong learning ability, high efficiency, and high accuracy. In this paper, we build models for accurate prediction of drug-abusing with the personality traits and some other information, based on logistic regression, decision tree, and random forest separately. We find out that the sensation of respondents and the country which they are from is the most important factor for drug abuse. And we can get a conclusion that drug abuse is not only depending on a person’s inner being, but also affected by the environment they lived in.
Codes from incidence matrices of some Bouwer graphs

We examine the [Formula: see text]-ary linear codes from incidence matrix of the Bouwer graph [Formula: see text] with vertex set [Formula: see text] and two vertices are adjacent if they can be written as [Formula: see text] and [Formula: see text] where either [Formula: see text] or [Formula: see text] differs from [Formula: see text] in exactly one position, say the [Formula: see text]th position, where [Formula: see text] All the main parameters of the codes are obtained as [Formula: see text] Also, we determine linear codes from incidence matrices of Bouwer graphs [Formula: see text] and all the main parameters of the codes are obtained as [Formula: see text] All the above codes can be used for full error correction by permutation decoding.
Learned and Controlled Autonomous Robotic Exploration in an Extreme, Unknown Environment

Exploring and traversing extreme terrain with surface robots is difficult, but highly desirable for many applications, including exploration of planetary surfaces, search and rescue, among others. For these applications, to ensure the robot can predictably locomote, the interaction between the terrain and vehicle, terramechanics, must be incorporated into the model of the robot's locomotion. Modeling terramechanic effects is difficult and may be impossible in situations where the terrain is not known a priori. For these reasons, learning a terramechanics model online is desirable to increase the predictability of the robot's motion. A problem with previous implementations of learning algorithms is that the terramechanics model and corresponding generated control policies are not easily interpretable or extensible. If the models were of interpretable form, designers could use the learned models to inform vehicle and/or control design changes to refine the robot architecture for future applications. This paper explores a new method for learning a terramechanics model and a control policy using a model-based genetic algorithm. The proposed method yields an interpretable model, which can be analyzed using preexisting analysis methods. The paper provides simulation results that show for a practical application, the genetic algorithm performance is approximately equal to the performance of a state-of-the-art neural network approach, which does not provide an easily interpretable model.
Sequential Recommender via Time-aware Attentive Memory Network

Recommendation systems aim to assist users to discover most preferred contents from an ever-growing corpus of items. Although recommenders have been greatly improved by deep learning, they still faces several challenges: (1) Behaviors are much more complex than words in sentences, so traditional attentive and recurrent models may fail in capturing the temporal dynamics of user preferences. (2) The preferences of users are multiple and evolving, so it is difficult to integrate long-term memory and short-term intent. In this paper, we propose a temporal gating methodology to improve attention mechanism and recurrent units, so that temporal information can be considered in both information filtering and state transition. Additionally, we propose a Multi-hop Time-aware Attentive Memory network (MTAM) to integrate long-term and short-term preferences. We use the proposed time-aware GRU network to learn the short-term intent and maintain prior records in user memory. We treat the short-term intent as a query and design a multi-hop memory reading operation via the proposed time-aware attention to generate user representation based on the current intent and long-term memory. Our approach is scalable for candidate retrieval tasks and can be viewed as a non-linear generalization of latent factorization for dot-product based Top-K recommendation. Finally, we conduct extensive experiments on six benchmark datasets and the experimental results demonstrate the effectiveness of our MTAM and temporal gating methodology.
Implementation of Encryption in a Video File and Integration of Extracted Frames into Gif Format

Piracy is a duplication of authorized contentwithout seeking permission. There are five main types of piracyCounter- feiting, Internet Piracy, End-User Piracy, Client-Server Overuse, and Hard-Disk Loading. Illegalduplication of the copyrighted video is mainly implemented in movies played in the theatre. With the Internetdeveloping at a rapid rate, internet piracy has become very common and is a growing problem. As the technologyis improving, security has become a serious concern. When a movie is released, the first pirates capture it using acamera which is known as videotaping. The videotaped content is either burned into a CD and circulated or it isuploaded on the Internet. The proposed ideology provides a solution to identify the pirated CD and the locationwhere it has been done using steganography techniques in combination with the knight’s tour algorithm [1]. Itfocuses on Encryption of data (sequence used) using Knight’s Tour algorithm and identifying video piracyusing,sequence and GIF. Knight’s Tour algorithm is used to encrypt data (8-bit password) behind the frames. An obtainedsequence is used for the selection of frames [2]. The frames extracted are converted into a GIF file and is mailed.GIF is used to identify piracy. The level of security provided to the system by applying the Knight Tour algorithmincreases the complexity. Hence it is difficult for the intruder from breaking the secret[3].
National Defense Education Resource Recommender of High Education Institutions Based on Knowledge-Aware Generative Adversarial Network

As there are issues of cold start and data sparsity in existing recommendation algorithms, this paper integrates the representation learning of knowledge graph into the recommendation process of generative adversarial model, and proposes a national defense education resource recommender of high education institutions based on knowledge-aware generative adversarial (KA-IRGAN). This model extracts the related entities and relations from the comment information of resource, and constructs the feature related knowledge sub graph of national defense education resource. Then the knowledge graph embedding method is utilized to transfer the entities in the knowledge sub graph as low-dimensional vectors, so as to obtain the low-dimensional feature vector of items and students for constructing the scoring function of item preference. The generative and discriminative modules are designed in the generative adversarial framework based on knowledge-aware. The generator and discriminator achieve Nash equilibrium in the process of iterative calculation, so that the final distribution of generative model is close to the real distribution of recommended resources. The experimental results on two benchmark datasets show that KA-IRGAN is superior to the existing IGRAN model in terms of recommendation accuracy and ranking quality, and is able to provide more efficient recommendation for national defense education system of high education institutions.
Assessing the Helpfulness of Learning Materials with Inference-Based Learner-Like Agent

Many English-as-a-second language learners have trouble using near-synonym words (e.g., small vs. little; briefly vs. shortly) correctly, and often look for example sentences to learn how two nearly synonymous terms differ.Prior work uses hand-crafted scores to recommend sentences but have difficulty in adopting such scores to all the near-synonyms as near-synonyms differ in various ways.We notice that the helpfulness of the learning material would reflect on the learners' performance.Thus, we propose the inference-based learner-like agent to mimic learner behavior and identify good learning materials by examining the agent's performance.To enable the agent to behave like a learner, we leverages entailment modeling's capability of inferring answers from the provided materials.Experimental results show that the proposed agent is equipped with good learner-like behavior to achieve the best performance in both fill-inthe-blank (FITB) and good example sentence selection tasks.We further conduct a classroom user study with college ESL learners.The results of the user study show that the proposed agent can find out example sentences that help students learn more easily and efficiently.Compared to other models, the proposed agent improves the score of more than 17% of students after learning.
The Impact of Dynamics of Collaborative Software Engineering on Introverts

Background: Collaboration among software engineers through face-to-face discussions in teams has been promoted since the adoption of agile methods. However, these discussions might demote the contribution of software engineers who are introverts, possibly leading to sub-optimal solutions and creating work environments that benefit extroverts. Objective: We aim to evaluate whether providing software engineers with time to work individually and reason about a collective problem is a setting that makes introverts more comfortable to interact and contribute more, ultimately leading to better solutions. Method: We plan to conduct a between-subjects study, with teams in a control group that design a software architecture in a team discussion meeting and teams in a treatment group in which subjects work individually before engaging in a meeting. We will assess and compare the amount of contribution of introverts, their subjective experiences, and the designed solutions. Limitations: As extroverts will be present in both groups, we will not be able to conclude that better solutions are solely due to the increased participation of introverts. The analyses of their subjective experience and amount of contributions might provide evidence to suggest the reasons for observed differences.
A Visualization Analysis Approach for Logistics Customer Maintenance

Customer maintenance plays a critical role in operating the logistics corporations. Motivated by the recent massive employment of data science in this field, analysts tend to solve customer maintenance problems using predictive modeling. Furthermore, data visualization is usually utilized as an interactive tool to involve domain knowledge in data-driven analysis to select highly relevant features for the sake of improving accuracy and reducing computational complexity. While approaches have been developed to observe the importance of features, it is of great interest to know how they impact the experience of customers. The existing methods such as scatterplot and paralleled coordinate plot for visualizing the correlation between attributes do not have a deep view of the data and the interpretation capability is limited. To address this issue, this paper investigates the underlying correlation between continuous features and binary class labels to facilitate classification modeling. A data visualization analysis framework is developed using weight of evidence (WOE), where a semi-supervised approach is proposed to discretize the continuous features for WOE computation targeting on the appropriate discovery and visualization of hidden data patterns. As shown in the experimental results, the developed visualization analysis framework demonstrates a superior interpretation capability compared to the existing methods.
Prescribing Deep Attentive Score Prediction Attracts Improved Student Engagement.

Intelligent Tutoring Systems (ITSs) have been developed to provide students with personalized learning experiences by adaptively generating learning paths optimized for each individual. Within the vast scope of ITS, score prediction stands out as an area of study that enables students to construct individually realistic goals based on their current position. Via the expected score provided by the ITS, a student can instantaneously compare one's expected score to one's actual score, which directly corresponds to the reliability that the ITS can instill. In other words, refining the precision of predicted scores strictly correlates to the level of confidence that a student may have with an ITS, which will evidently ensue improved student engagement. However, previous studies have solely concentrated on improving the performance of a prediction model, largely lacking focus on the benefits generated by its practical application. In this paper, we demonstrate that the accuracy of the score prediction model deployed in a real-world setting significantly impacts user engagement by providing empirical evidence. To that end, we apply a state-of-the-art deep attentive neural network-based score prediction model to Santa, a multi-platform English ITS with approximately 780K users in South Korea that exclusively focuses on the TOEIC (Test of English for International Communications) standardized examinations. We run a controlled A/B test on the ITS with two models, respectively based on collaborative filtering and deep attentive neural networks, to verify whether the more accurate model engenders any student engagement. The results conclude that the attentive model not only induces high student morale (e.g. higher diagnostic test completion ratio, number of questions answered, etc.) but also encourages active engagement (e.g. higher purchase rate, improved total profit, etc.) on Santa.
Experimental demonstration of a quantum model learning agent

Developing novel quantum devices poses the problem of their efficient characterization. We introduce and experimentally demonstrate a methodology to automatically formulate and rank Hamiltonian models, learning the most appropriate in reproducing the observed system’s dynamics.
A fast method for detecting minority structures in a graph

A graph contains plentiful structures. Some minority structures are important, such as high degree nodes and bridges. Detecting these minority structures is beneficial to accelerate computational graph analysis and improve the comprehension of graph visualization. Regarding four typical minority structures, this paper proposes two algorithms to detect these structures fast and efficiently. A set of experiments demonstrate the effectiveness of the proposed algorithms.
Outcome Indistinguishability

Prediction algorithms assign numbers to individuals that are popularly understood as individual "probabilities" -- what is the probability of 5-year survival after cancer diagnosis? -- and which increasingly form the basis for life-altering decisions. Drawing on an understanding of computational indistinguishability developed in complexity theory and cryptography, we introduce Outcome Indistinguishability. Predictors that are Outcome Indistinguishable yield a generative model for outcomes that cannot be efficiently refuted on the basis of the real-life observations produced by Nature. We investigate a hierarchy of Outcome Indistinguishability definitions, whose stringency increases with the degree to which distinguishers may access the predictor in question. Our findings reveal that Outcome Indistinguishability behaves qualitatively differently than previously studied notions of indistinguishability. First, we provide constructions at all levels of the hierarchy. Then, leveraging recently-developed machinery for proving average-case fine-grained hardness, we obtain lower bounds on the complexity of the more stringent forms of Outcome Indistinguishability. This hardness result provides the first scientific grounds for the political argument that, when inspecting algorithmic risk prediction instruments, auditors should be granted oracle access to the algorithm, not simply historical predictions.
Suicidal Intent Prediction Using Natural Language Processing (Bag of Words) Approach

The Bag of Words (BOW) technique is a Natural Language Processing technique that is applied to learn from the text. The basic idea of the Bag of Words is to take any text and count the frequency of words and place those unique words in a dictionary. This research is carried out to predict the causes of suicide in Nigeria by using the Bag of Words (BOW) technique together with three machine learning algorithms. The logistic regression algorithm, Random Forest classifier, and XGBoost classifier are the machine learning algorithms considered for this dataset. For feature extraction, the TfidfVectorizer has been used. The result gotten shows that the logistic regression classifier is suitable for the dataset used in this study, therefore, for similar datasets, the logistic regression together with TfidfVectorizer can be used. However, the logistic regression gave an accuracy score of 89% leaving an 11% chance of misclassification.
Segmentation and Entropy Coding Analysis of a Data Compression System for Power Quality Disturbances

Amidst the huge data generated from power systems monitoring structures, the demand for better data storage and streaming emerged. In this paper the problem is characterized and a solution is proposed. Utilizing signal processing methods into the field of energy quality, is possible to compress and segment the power signal. Transformations such as wavelets transforms and entropy encoders are applied in a processing pipeline to reduce the bandwidth necessary to transfer the data.
Efficient Exploration by Novelty-Pursuit

Efficient exploration is essential to reinforcement learning in tasks with huge state space and long planning horizon. Recent approaches to address this issue include the intrinsically motivated goal exploration processes (IMGEP) and the maximum state entropy exploration (MSEE). In this paper, we propose a goal-selection criterion in IMGEP based on the principle of MSEE, which results in the new exploration method novelty-pursuit. Novelty-pursuit performs the exploration in two stages: first, it selects a seldom visited state as the target for the goal-conditioned exploration policy to reach the boundary of the explored region; then, it takes random actions to explore the non-explored region. We demonstrate the effectiveness of the proposed method in environments from simple maze environments, MuJoCo tasks, to the long-horizon video game of SuperMarioBros. Experiment results show that the proposed method outperforms the state-of-the-art approaches that use curiosity-driven exploration.
A Simple Ensemble Learning Knowledge Distillation

Deep neural network (DNN) has shown significant improvement in learning and generalizing different machine learning tasks over the years. But it comes with an expense of heavy computational power and memory requirements. We can see that machine learning applications are even running in portable devices like mobiles and embedded systems nowadays, which generally have limited resources regarding computational power and memory and thus can only run small machine learning models. However, smaller networks usually do not perform very well. In this paper, we have implemented a simple ensemble learning based knowledge distillation network to improve the accuracy of such small models. Our experimental results prove that the performance enhancement of smaller models can be achieved through distilling knowledge from a combination of small models rather than using a cumbersome model for the knowledge transfer. Besides, the ensemble knowledge distillation network is simpler, time-efficient, and easy to implement.