Reviewerly/exHarmony huggingface dataset:
Deep Multi-Task Transfer Network for Cross Domain Person Re-Identification

As a prominent application of surveillance video analysis, person re-identification attracts much more research attention recently.Existing person re-identification models often focus on supervision by the pedestrian identity annotation, while it has limited scalability in realistic.Though several unsupervised person re-identification researches pay attention to solve this problem, they are either clustering based or cross domain based approaches, where a conventional assumption of them is the identity number of the target dataset is acknowledged.To relax this hypothesis, we propose a Deep Multi-task Transfer Network (DMTNet) for cross domain person re-identification, which conduct classification, attribute attention and identification task between source and target domains.There are three main novelties in DMTNet, including clustering number estimating algorithm to learn prior knowledge from source data to estimate the identity number, attribute attention importance learning rather than directly utilizing attribute information, and a multi-task transfer learning mechanism to transfer specific tasks cross domains.To prove the superiority of our DMTNet, we implement several compared experiments on DukeMTMC-reID and Market-1501 datasets, which results show the advancement of our network.Moreover, the discussions for different modules also point out the significance of the specific tasks.
A Study on Video Game Review Summarization

Game reviews have constituted a unique means of interaction between players and companies for many years.The dynamics appearing through online publishing have significantly grown the number of comments per game, giving rise to very interesting communities.The growth has, in turn, led to a difficulty in dealing with the volume and varying quality of the comments as a source of information.This work studies whether and how game reviews can be summarized, based on the notions pre-existing in aspect-based summarization and sentiment analysis.The work provides suggested pipeline of analysis, also offering preliminary findings on whether aspects detected in a set of comments can be consistently evaluated by human users.
Distances between intuitionistic fuzzy sets of second type with application to diagnostic medicine

The concept of intuitionistic fuzzy sets of second type (IFSST) generalizes intuitionistic fuzzy sets (IFS) and thus, has many applications in decision making problems.The main feature of IFSST is that it is characterized by three parameters, namely: membership degree, non-membership degree and degree of indeterminacy in such a way that the sum of the square of each of the parameters is one.The purpose of this paper is to present the axiomatic definition of distance between IFSST, taking into account the three parameters that describe the sets and to investigate numerically, the validity of some distances between intuitionistic fuzzy sets introduced by E. Szmidt and J. Kacprzyk in IFSST environment.Finally, we explore the application of IFSST in diagnostic medicine by employing normalized Hamming distance of IFSST to calculate the distance between patients and diseases, because it provides a reliable distance with respect to other distances.Actually, by using the distance between patients and diseases (both in IFSST values), with recourse to the corresponding symptoms observe in the patients and of the diseases, we determine the illness of the paients.These distances are suggestible to be deployed in solving multicriteria decision making problems.
Determinants of Bitcoin prices

Purpose -The increase in the popularity of cryptocurrency market, various literature figure out the macroeconomic factors that effect the price movements of cryptocurrencies.This research aims to identify the interaction between gold, brent oil and bitcoin.Methodology -The database includes the Daily prices of Bitcoin, gold and brent oil prices between the period of 28.04.2013-23.07.2019 which consist of 484 daily data.Natural logaritm for each indicator is used.First, the stationarity of the series were analyzed with ADF (Augmented Dickey Fuller) unit root test.Lag lengths are determined.Interactions between the series were analyzed by the Impulse-Response Function and Variance Decomposition methods.Findings-The series are found out to be stationary at first difference.Impulse response graphs indicate that all variables respond in a reducing way to reducing shocks occurred in each indicator.Shocks have lost their effect on average in 5 days. Conclusion-The results indicate that the effect of gold and brent oil prices on bitcoin daily prices do not have a strong effect.The results may be beneficial for investors to consider diversification for the portfolios.
Path Ranking with Attention to Type Hierarchies

The objective of the knowledge base completion problem is to infer missing information from existing facts in a knowledge base. Prior work has demonstrated the effectiveness of path-ranking based methods, which solve the problem by discovering observable patterns in knowledge graphs, consisting of nodes representing entities and edges representing relations. However, these patterns either lack accuracy because they rely solely on relations or cannot easily generalize due to the direct use of specific entity information. We introduce Attentive Path Ranking, a novel path pattern representation that leverages type hierarchies of entities to both avoid ambiguity and maintain generalization. Then, we present an end-to-end trained attention-based RNN model to discover the new path patterns from data. Experiments conducted on benchmark knowledge base completion datasets WN18RR and FB15k-237 demonstrate that the proposed model outperforms existing methods on the fact prediction task by statistically significant margins of 26% and 10%, respectively. Furthermore, quantitative and qualitative analyses show that the path patterns balance between generalization and discrimination.
Ontology-Mediated Queries from Examples: a Glimpse at the DL-Lite Case

Reverse engineering queries from given data, as in the case of query-by-example and query definability, is an important problem with many applications that has recently gained attention in the areas where symbolic artificial intelligence meets learning. In the presence of ontologies this problem was recently studied for Horn-ALC and Horn-ALCI. The main contribution of this paper is to take a first look at the case of DL-Lite, to identify cases where the addition of the ontology does not increase the worst-case complexity of the problem. Unfortunately, reverse engineering conjunctive queries is known to be very hard, even for plain databases, since the smallest witness query is known to be exponential in general. In the light of this, we outline some possible research directions for exploiting the ontology in order to obtain smaller witness queries.
Adversarial Localized Energy Network for Structured Prediction

This paper focuses on energy model based structured output prediction. Though inheriting the benefits from energy-based models to handle the sophisticated cases, previous deep energy-based methods suffered from the substantial computation cost introduced by the enormous amounts of gradient steps in the inference process. To boost the efficiency and accuracy of the energy-based models on structured output prediction, we propose a novel method analogous to the adversarial learning framework. Specifically, in our proposed framework, the generator consists of an inference network while the discriminator is comprised of an energy network. The two sub-modules, i.e., the inference network and the energy network, can benefit each other mutually during the whole computation process. On the one hand, our modified inference network can boost the efficiency by predicting good initializations and reducing the searching space for the inference process; On the other hand, inheriting the benefits of the energy network, the energy module in our network can evaluate the quality of the generated output from the inference network and correspondingly provides a resourceful guide to the training of the inference network. In the ideal case, the adversarial learning strategy makes sure the two sub-modules can achieve an equilibrium state after steps. We conduct extensive experiments to verify the effectiveness and efficiency of our proposed method.
Stochastically Robust Personalized Ranking for LSH Recommendation Retrieval

Locality Sensitive Hashing (LSH) has become one of the most commonly used approximate nearest neighbor search techniques to avoid the prohibitive cost of scanning through all data points. For recommender systems, LSH achieves efficient recommendation retrieval by encoding user and item vectors into binary hash codes, reducing the cost of exhaustively examining all the item vectors to identify the top-k items. However, conventional matrix factorization models may suffer from performance degeneration caused by randomly-drawn LSH hash functions, directly affecting the ultimate quality of the recommendations. In this paper, we propose a framework named øurmodel, which factors in the stochasticity of LSH hash functions when learning real-valued user and item latent vectors, eventually improving the recommendation accuracy after LSH indexing. Experiments on publicly available datasets show that the proposed framework not only effectively learns user's preferences for prediction, but also achieves high compatibility with LSH stochasticity, producing superior post-LSH indexing performances as compared to state-of-the-art baselines.
Deep Conservative Policy Iteration

Conservative Policy Iteration (CPI) is a founding algorithm of Approximate Dynamic Programming (ADP). Its core principle is to stabilize greediness through stochastic mixtures of consecutive policies. It comes with strong theoretical guarantees, and inspired approaches in deep Reinforcement Learning (RL). However, CPI itself has rarely been implemented, never with neural networks, and only experimented on toy problems. In this paper, we show how CPI can be practically combined with deep RL with discrete actions, in an off-policy manner. We also introduce adaptive mixture rates inspired by the theory. We experiment thoroughly the resulting algorithm on the simple Cartpole problem, and validate the proposed method on a representative subset of Atari games. Overall, this work suggests that revisiting classic ADP may lead to improved and more stable deep RL algorithms.
Large-Screen Interactive Technology With 3D Sensor Based on Clustering Filtering Method and Unscented Kalman Filter

In this paper, a large screen interactive 3D sensor technology with clustering filtering method and unscented Kalman filter has been addressed. The surface and interactive area with the screen is mapped together using image processing and machine vision processing of 3D sensor. Then, the predictable and accurate multi-point interaction can be obtained. Also some operations can be achieved such as zooming, panning, selecting and moving image components, etc. Vision method, infrared induction, dynamic capture, image processing and other mixed technologies have been simultaneously utilized to recognize human gestures and simulate mouse event which can give participants a fully interactive experience. Experimental results show that this large-screen interactive technology with 3D sensor can achieve accurate multi-point interaction and gesture control, and have good human-machine interaction effect.
Theory-Based Causal Transfer:Integrating Instance-Level Induction and Abstract-Level Structure Learning

Learning transferable knowledge across similar but different settings is a fundamental component of generalized intelligence. In this paper, we approach the transfer learning challenge from a causal theory perspective. Our agent is endowed with two basic yet general theories for transfer learning: (i) a task shares a common abstract structure that is invariant across domains, and (ii) the behavior of specific features of the environment remain constant across domains. We adopt a Bayesian perspective of causal theory induction and use these theories to transfer knowledge between environments. Given these general theories, the goal is to train an agent by interactively exploring the problem space to (i) discover, form, and transfer useful abstract and structural knowledge, and (ii) induce useful knowledge from the instance-level attributes observed in the environment. A hierarchy of Bayesian structures is used to model abstract-level structural causal knowledge, and an instance-level associative learning scheme learns which specific objects can be used to induce state changes through interaction. This model-learning scheme is then integrated with a model-based planner to achieve a task in the OpenLock environment, a virtual “escape room” with a complex hierarchy that requires agents to reason about an abstract, generalized causal structure. We compare performances against a set of predominate model-free reinforcement learning (RL) algorithms. RL agents showed poor ability transferring learned knowledge across different trials. Whereas the proposed model revealed similar performance trends as human learners, and more importantly, demonstrated transfer behavior across trials and learning situations.1
WEARABLE ACTIVITY-TRACKING DEVICE FEATURE PREFERENCE AMONGST SOUTH AFRICAN GENERATION Y STUDENTS

Wearable activity-tracking devices such as pedometers, various electrode-based chest straps, accelerometer-based arm straps, fashion bracelets, jewellery, fitness bands and watches, earphones, and smart clothing have revolutionised health and sports monitoring. Based on the benefits of using this wearable technology, it is no surprise that the adoption thereof has increased rapidly. In 2019, the sports, fitness, and activity monitor market is estimated to generate 2.8 billion USD in global revenue. In South Africa, merely 13 percent of households own some form of wearable technology. Dominating this market is the youth, where 33.7 percent of these individuals are between the ages of 18 to 24 years, thus belonging to the Generation Y cohort. The literature, documenting wearable activity-tracking device feature preference amongst consumers, especially among this cohort, is limited. As such, this study explored South African Generation Y students' feature preferences on wearable activity-tracking devices in order to assist device manufacturers and marketing practitioners in developing and marketing devices that will appeal to this large segment. A non-probability convenience sample of 480 students, registered at three public HEIs in South Africa’s Gauteng province, voluntarily completed self-administered questionnaires. A descriptive research design was followed and the captured data were analysed using measures of frequency. The findings indicate that the top five device features preferred by South African Generation Y students are measuring heart rate/blood pressure, tracking steps taken/distance travelled, calculating daily calories burnt, tracking sleep patterns and GPS tracking. In an effort to increase wearable activity-tracking device adoption amongst Generation Y students, both local and international device manufacturers need to consider these device feature preferences in order to manufacture and advertise such devices accordingly.
Photovoltaic Solar Spectral Filtering Using Bio-Filters

The harsh solar irradiance measurement in recent times has further supported the existence of global climate change. It is not clear if this challenge is located in other parts of the world. Over the years, it has been observed that PV panel have a lower lifespan with a failing performance. Due to the peculiarity of this challenge in the research, the bio-filter was synthesized to shield the PV panel from damage via the aforementioned harsh solar radiation. The bio-filter was tested using the monocrystalline and polycrystalline panel. The results proved that the bio-filters are good stabilizing medium.
Performance Analysis of Resampling Techniques on Class Imbalance Issue in Software Defect Prediction

Predicting the defects at early stage of software development life cycle can improve the quality of end product at lower cost.Machine learning techniques have been proved to be an effective way for software defect prediction however an imbalance dataset of software defects is the main issue of lower and biased performance of classifiers.This issue can be resolved by applying the re-sampling methods on software defect dataset before the classification process.This research analyzes the performance of three widely used resampling techniques on class imbalance issue for software defect prediction.The resampling techniques include: "Random Under Sampling", "Random Over Sampling" and "Synthetic Minority Oversampling Technique (SMOTE)".For experiments, 12 publically available cleaned NASA MDP datasets are used with 10 widely used supervised machine learning classifiers.The performance is evaluated through various measures including: F-measure, Accuracy, MCC and ROC.According to results, most of the classifiers performed better with "Random Over Sampling" technique in many datasets.
Malicious Domain Detection via Domain Relationship and Graph Models

Malicious domain is a vital component of various cyber attacks. Recent techniques detect malicious domains by building classifiers based on domain character features which may be easily evaded by attackers. In this paper, we propose a malicious domain detection approach based on domain relationship features, PDNS features, and domain character features. The key insight is that malicious domains deploy on IP that is loosely regulated and the domains on such IP have similar network characteristics including domain relationships, resolution characteristics, and network behaviors. We find that the relationship of malicious domains is different from that of benign domains. Take this into account, we build meaningful associations among domains and extract the domains relationship features by a modified graph embedding algorithm from Passive DNS data. Besides, we mine more features from PDNS which have not been mentioned in previous work. These PDNS features can enhance the effectiveness of the classifier. Finally, we combine domain character features, PDNS features and relationship features as the feature set. We evaluate the performance of our model on a real-world dataset from DNS servers. We achieve excellent performance by applying several classifiers based on domain character features, PDNS features and relationship features with an accuracy of 94.0%, a recall of 94.3% and a precision of 93.8% in the challenging scenario where domains deploy on the same IP and malicious domains share similar character features with benign domains. We also compare our method with two state-of-the-art detection approaches and find that our approach outperforms those SOTA approaches. Based on the comparison results, we point out that our way to construct a domain relationship graph can effectively mine the domain association features and the features combined with PDNS features and domain character features can effectively identify malicious domains which are similar to benign domains.
YOLOv3-Darknet with Adaptive Clustering Anchor Box for Intelligent Dry and Wet Garbage Identification and Classification

The implementation of dry and wet garbage classification is conducive to reducing waste land occupation, reducing pollution and improving the utilization rate of resources, which has social, economic and ecological benefits. At present, there are many shortcomings in manual classification, such as: high work intensity, poor environment, high cost. In the traditional detection method, the robustness is poor, the accuracy is low, can't achieve rapid detection in a complex background. We propose an anchor box and yolov3-darknet model based on adaptive data clustering to identify, classify and detect dry and wet garbage. The experimental results show that our method can adapt to the classification of dry and wet garbage in complex environment, and can accurately and quickly identify the dry and wet garbage, ensure the purity of wet garbage above 90%, and meet the requirements of secondary treatment of garbage.
Cholesky Decomposition-Based Metric Learning for Video-Based Human Action Recognition

Video-based human action recognition can understand human actions and behaviours in the video sequences, and has wide applications for health care, human-machine interaction and so on. Metric learning, which learns a similarity metric, plays an important role in human action recognition. However, learning a full-rank matrix is usually inefficient and easily leads to overfitting. In order to overcome the above issues, a common way is to impose the low-rank constraint on the learned matrix. This paper proposes a novel Cholesky decomposition based metric learning (CDML) method for effective video-based human action recognition. Firstly, the improved dense trajectories technique and the vector of locally aggregated descriptor (VLAD) are respectively used for feature detection and feature encoding. Then, considering the high dimensionality of VLAD features, we propose to learn a similarity matrix by taking advantage of Cholesky decomposition, which decomposes the matrix into the product between a lower triangular matrix and its symmetric matrix. Different from the traditional low-rank metric learning methods that explicitly adopt the low-rank constraint to learn the matrix, the proposed algorithm achieves such a constraint by controlling the rank of the lower triangular matrix, thus leading to high computational efficiency. Experimental results on the public video dataset show that the proposed method achieves the superior performance compared with several state-of-the-art methods.
Modeling and optimization of biosorption of lead (II) ions from aqueous solution onto pine leaves (Pinus kesiya) using response surface methodology

In this work, the central composite design in response surface methodology by the Design-Expert software was used for optimizing the removal of Pb(II) ions from aqueous solution by pine leaves (Pinus kesiya).Effects of pH solution , adsorption time and initial Pb(II) ions concentration on adsorption capacity were investigated.Experimental data were fitted by using five nonlinear isotherm models including Langmuir, Freundlich, Sips, Temkin and Dubinin-Radushkevich.The maximum Pb(II) adsorption capacity (q max ) estimated from the Langmuir isotherm model was 31.04 mg/g, which is higher than other biomaterials such as barley straw, Cucumis sativus peel, coconut tree sawdust, etc. Kinetic studies indicated that the uptake of Pb(II) occurred on the Elovich model within two stages.Thermodynamic studies at different temperatures showed the biosorption to be endothermic and spontaneous.The study concluded that P. kesiya can be a good adsorbent for removing Pb(II) from aqueous solution.