Reviewerly/exHarmony huggingface dataset:
Authenticating Facebook Users Based on Widget Interaction Behavior

Facebook has become an important part of our daily life. From knowing the status of our relatives, showing off a new car, to connecting with a high school classmate, abundant personally identifiable information (PII) are made visible to others by posts, images and news. However, this free flow of information has also created significant cyber-security challenges that make us vulnerable to social engineering and cyber crimes. To confront these challenges, we propose a new behavioral biometric that verifies a user based on his or her widget interaction behavior when using Facebook. Specifically, we monitor activities on the user's Facebook account using our own logging software and verify the user's claimed identity by binary classifiers trained with two algorithms (SVM-rbf and the GBM- Gradient Boosting Machines). Our novel dataset consists of eight users over a month of data collection with an average of 2.95k rows of data per user. We convert these activities data into meaningful features such as day-of-week, hour-of-day, and widget types and duration of mouse staying on a widget. The performance shows that our novel widget interaction modality is promising for authentication. The SVM-rbf classifiers achieve a mean Equal Error Rate (EER) and mean Accuracy (ACC) of 3.91% and 97.79%, while the GBM classifiers a mean EER and ACC of 2.76% and 97.88%, respectively. In addition, we perform an ablation study to understand the impact of individual features on authentication performance. The importance of features are ranked in the descending order of hour-of-day, day-of-week, and widget types and duration.
Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation

Autonomous systems need to localize and track surrounding objects in 3D space for safe motion planning. As a result, 3D multi-object tracking (MOT) plays a vital role in autonomous navigation. Most MOT methods use a tracking-by-detection pipeline, which includes both the object detection and data association tasks. However, many approaches detect objects in 2D RGB sequences for tracking, which lacks reliability when localizing objects in 3D space. Furthermore, it is still challenging to learn discriminative features for temporally consistent detection in different frames, and the affinity matrix is typically learned from independent object features without considering the feature interaction between detected objects in the different frames. To settle these problems, we first employ a joint feature extractor to fuse the appearance feature and the motion feature captured from 2D RGB images and 3D point clouds, and then we propose a novel convolutional operation, named RelationConv, to better exploit the correlation between each pair of objects in the adjacent frames and learn a deep affinity matrix for further data association. We finally provide extensive evaluation to reveal that our proposed model achieves state-of-the-art performance on the KITTI tracking benchmark.
Intervention Recommendation for Improving Disability Employment

In the disability employment service, an emerging challenge is to recommend the skills whose improvement will increase the employment perspective most. The process of a skill being improved is called an intervention and different skills are called factors. The problem involves recommendation for outcome improvement, which requires estimating the improvement in the employment perspective, i.e., the outcome, driven by interventions on recommended factors. Currently, most recommendation systems deployed for the employment service rely on traditional recommendation models where the desired outcome instead of the degree of outcome improvement is the main goal for optimization. In this paper, we present a causality-based approach for recommending factors for intervention to achieve the largest improvement in the employment potential of disabled job seekers. It involves inferring the causal effect of interventions on the employment outcome to make recommendations for individuals. The causal interpretation of our model can justify given recommendations. We conduct a case study with our industry partner in the disability employment service. Results show that the recommended interventions could improve the employability of disabled job seekers. Experiments are also carried out with datasets in other domains to demonstrate the promise of our approach in different applications.
Improving and benchmarking of algorithms for Γ-maximin, Γ-maximax and interval dominance

Γ-maximin, Γ-maximax and interval dominance are familiar decision criteria for making decisions under severe uncertainty, when probability distributions can only be partially identified. One can apply these three criteria by solving sequences of linear programs. In this study, we present new algorithms for these criteria and compare their performance to existing standard algorithms. Specifically, we use efficient ways, based on previous work, to find common initial feasible points for these algorithms. Exploiting these initial feasible points, we develop early stopping criteria to determine whether gambles are either Γ-maximin, Γ-maximax or interval dominant. We observe that the primal-dual interior point method benefits considerably from these improvements. In our simulation, we find that our proposed algorithms outperform the standard algorithms when the size of the domain of lower previsions is less or equal to the sizes of decisions and outcomes. However, our proposed algorithms do not outperform the standard algorithms in the case that the size of the domain of lower previsions is much larger than the sizes of decisions and outcomes.
Image Retrieval Scheme Using Efficient Fusion of Color and Shape Moments

Due to the tremendous increaseVarish, Naushad in the digital image data, the efficient and effective image content-based search scheme for retrieving desired images from a large image repository is highly required. The biggest challenge in image retrieval scheme is to retrieveSingh, Priyanka the desired multimedia images from the digital image repository with minimum time. Extracting significant image features with low dimensional feature descriptor play a significant role in improving retrieval outcomes. In the presented paper, an image retrieval scheme is proposed using fused low dimensional feature descriptor which is obtained by fusion of probability histogram-based HSV color moments and multiresolution based shape moments. The color moments and shape moments are extracted from the Laplacian filter based preprocessed image. The suggested scheme is implemented on a standard Corel-1K image dataset and the retrieval accuracy is measured using precision, recall, and F-score metrics. The experimental outcomes are also validated and compared with some existing state of the art image retrieval schemes and it outperforms over the existing ones.
Modeling elderly behavioral patterns in single-person households

Abstract This paper proposes and describes an unsupervised computational model that monitors an elderly person who lives alone and issues alarms when a risk to the elderly person’s well-being is identified. This model is based on data extracted exclusively from passive infrared motion sensors connected to a ZigBee wireless network. The proposed monitoring system and model is non-intrusive, does not capture any images, and does not require any interaction with the monitored person. Thus, it is more likely to be adopted by members of the elderly population who might reject other more intrusive or complex types of technology. The developed computational model for activity discovery employs a kernel estimator and local outlier factor calculation, which are reliable and have a low computational cost. This model was tested with data collected over a period of 25 days from two elderly volunteers who live alone and have fairly different routines. The results demonstrate the model’s ability to learn relevant behaviors, as well as identify and issue alarms for atypical activities that can be suggestive of health problems. This low-cost, minimalistic sensor network approach is especially suited to the reality of underdeveloped (and developing) countries where assisted living communities are not available and low cost and ease of use are paramount.
A novel model for semantic similarity measurement based on wordnet and word embedding

To measure semantic similarity between words, a novel model DFRVec that encodes multiple semantic information of a word in WordNet into a vector space is presented in this paper. Firstly, three different sub-models are proposed: 1) DefVec: encoding the definitions of a word in WordNet; 2) FormVec: encoding the part-of-speech (POS) of a word in WordNet; 3) RelVec: encoding the relations of a word in WordNet. Then by combining the three sub-models with an existing word embedding, the new model for generating the vector of a word is proposed. Finally, based on DFRVec and the path information in WordNet, a new method DFRVec+Path to measure semantic similarity between words is presented. The experiments on ten benchmark datasets show that DFRVec+Path can outperform many existing methods on semantic similarity measurement.
Testing QA Systems’ ability in Processing Synonym Commonsense Knowledge

'Synonym' is an imperative instrument of commonsense knowledge that we apply to make a good sense and sound judgement of our reading. To investigate the ability of machine comprehension models in handling the synonym commonsense knowledge, we developed an innovative approach to automatically generate a dataset based on the Stanford Question Answering Dataset (SQuAD 2.0). The brand-new dataset consists of additional distracting sentences or questions spawned using synonym commonsense knowledge. We formulated new questions by replacing noun entities of the original ones in SQuAD 2.0 with their synonyms. This approach followed the two fundamental principles of SQuAD 2.0 dataset: relevancy and plausibility (incorrect answers are more challenging if they are relevant and plausible). It improves the robustness/abstraction of the question set. To improve the synonym selection strategy in Word Sense Disambiguation (WSD) problem, we designed a new algorithm Multiple Source Adapted Lesk Algorithm (MSALA). Rather than only using WordNet as the source of gloss for adapted Lesk algorithm, we used both lexical database WordNet and commonsense database ConceptNet. This fusion provides a rich hierarchy of semantic relations for the MSALA algorithm. Using this method, we devised 11,000 questions and evaluated the performance of the state-of-the-art question answering system-BERT. Our result shows that the accuracy of the contemporary BERT-Base model dropped from 74.98% to 63.24%. This 10+% accuracy drop revealed the limitations of BERT in handling synonym commonsense knowledge.
Naive Bayes Approach for Retrieval of Video Object Using Trajectories

The popularity of video recordings either on mobile devices or video surveillance has contributed to the demand for video data applications. As a result, the management of video has become significant in the object retrieval process. Video object retrieval is seen as a major issue in the management of video objects. Object tracking is therefore a significant step in detecting and tracking video objects. Hence, object tracking is a major step, which detects and tracks the video objects using a hybrid model named Nearest Search Algorithm–Nonlinear Autoregressive Exogenous Neural Network. The tracked events are further employed for training, and the recognized events interrelated to the query is processed by proposed Naïve Bayes classifier for retrieving the highly relevant object trajectories and the events associated with it. The performance of the proposed Naïve Bayes classifier is greater when compared with other existing techniques with maximal precision of 81.985%, recall of 85.451%, and F-measure of 86.847%, respectively.
Provable Related-Key Security of Contracting Feistel Networks

We continue the line of works constructing related-key secure PRFs (Bellare and Cash, CRYPTO 2010) and PRPs (Barbosa and Farshim, FSE 2014). In detail, we consider generalized Feistel networks using contracting round functions from $$\{0,1\}^m$$ to $$\{0,1\}^n$$ , and explore conditions that are sufficient for such Contracting Feistel Networks (CFNs) to achieve security up to $$2^{n/2}$$ adversarial queries. As results, we show that provable related-key security is achieved with $$\lceil \frac{m}{n}\rceil +3$$ rounds, as long as the CFN uses two independent main keys $$K_1,K_2$$ in all the rounds in a close-to-alternating manner. Our results provide new approaches to construct related-key secure variable-input-length block ciphers from related-key secure variable-input-length PRFs.
Runtime Estimation of Application Memory Latency for Performance Analysis and Optimization

Various runtime factors impact memory latency and consequently impact application performance. Unfortunately the causal relationship is buried especially at runtime. In this paper we propose a new method for runtime estimation of application memory latency which helps discover the causal relationship. The new method leverages the hardware performance counters to calculate the average time that memory requests wait before getting fulfilled. We evaluate the method empirically in multiple scenarios and the estimation closely approximates the ground truth. We further demonstrate two examples of using the runtime estimation of application memory latency in application performance optimization and analysis, one in mitigating memory access interference in workload co-location and the other in dissecting the performance problem in the memory subsystem.
Long-Term Care Risk Prediction by Claims Data Analysis

In an aging society such as Japan and other OECD countries, the medical and non-medical costs of long-term care are increasing every year, and it is becoming more difficult for local governments to bear the costs. It is necessary to identify high-risk persons before they need long-term care. We built a prediction model for seven scenarios regarding the need for long-term care using medical claims, pharmacy claims, diagnosis procedure combination (DPC) payment system data, health examination results, and long-term care claims from a local government database. Based on the number of actual data, our proposed long-term care risk prediction is targeted at the elderly over 75 years old. Because there are many variables in the data, especially in medical claims, we used the heterogeneous mixture learning (HML) model because it can automatically optimize all combinations of explanatory variables. The explanatory variables we used are age, sex, 533 diagnosis codes as listed in ICD-10, 108 prescription drug groups under the therapeutic category of drugs in Japan, and 28 special health examination items. The results showed that the area under the curves (AUCs) for all scenarios was above 0.7. Since the recalls of HML were larger than that of other machine learning models, more high-risk persons can be identified by our model.
Mining Consistent, Non-Redundant and Minimal Negative Rules Based on Minimal Generators

Essentially, any negative association rules are constructed over infrequent underling itemsets, thus the number of valid negative rules is always quite huge, and effective extraction of negative rules is quite difficult. In this paper, we study an efficient mining algorithm of negative rules over minimal generators, where a set of negative rules can be effectively compressed by using minimal generators, especially for dense data. We first propose new two concepts, so called consistency and non-redundancy, for a set consisting of both positive and negative association rules. Next, we prove some a fundamental but important property for minimal generators, which enables us to construct a suffix tree over minimal generators. We show a new top-down algorithm for mining a consistent and non-redundant set of negative association rules. The top-down search uses the suffix tree in order to restrict some negative rules to be right-minimal, which also suppress the burst of negative rules. Finally, we show preliminary results of experimental evaluation for the proposed negative rule mining method.
StreamTeam-Football: Analyzing Football Matches in Real-Time on the Basis of Position Streams

In recent years, Big Data has become an important topic in many areas of our daily lives, including sports. Almost all professional clubs analyze matches to improve the performance of their teams. However, events are still predominantly captured manually, although many sensor-based and video-based tracking systems exist which provide the positions of the players and the ball in real-time. This manual process is tedious and error-prone. In this paper, we present STREAMTEAM-FOOTBALL, an open source football analysis application, that fills this gap. STREAMTEAM-FOOTBALL allows to analyze football matches fully automatically and in real-time on the basis of tracked position data using a data stream analysis approach. Our evaluation confirms the effectiveness of our automated analysis.
Survey on Context-Aware Healthcare Systems

Smart healthcare has promoted E-healthcare to a higher level in which healthcare and treatment services are ubiquitously and intelligently offered to users. Context-awareness is an important feature in smart healthcare systems that firstly deals with extracting current contextual information of users including patients, doctors, and nurses. Afterwards, by being aware of current context and situation of users, smart healthcare systems are able to provide autonomous and intelligent services to them. Due to the importance of this subject, several context-aware healthcare systems have been proposed in various aspects of healthcare and treatment; however, to our best knowledge not any survey paper has investigated and reviewed them. In this regards, this paper studies context-aware healthcare systems from four central viewpoints including environment, service type, context type and context source. The aim is to technically discuss and classify context-aware healthcare systems, to identify the advancements and shortcomings, and to illuminate the route for future research directions in this area.
The model for computation of complex technical objects parameters based on subdefinite calculations

Abstract The generalized computational model for calculation of complex technical object parameters when solving the problem of monitoring and their functioning control has been realized. The model has the following features: realization in the context of calculations of subdefinite expansion of parameters values based on assignment function and availability of consideration objects structural dynamics
Towards Robust Speech-to-Text Adversarial Attack

This paper introduces a novel adversarial algorithm for attacking the advanced speech-to-text transcription systems. Our proposed approach is based on developing an extension for the conventional distortion condition of the general adversarial optimization formulation using the Cramér integral probability metric. Minimizing over such a metric contributes to crafting signals very close to the subspace of legitimate speech recordings. That helps yield more robust adversarial signals against over-the-air playbacks without employing neither costly expectation over transformations nor static room impulse response simulations. Our approach considerably outperforms other targeted and non-targeted algorithms in terms of word error rate and sentence-level accuracy. Furthermore compared to seven other strong white and black-box adversarial attacks, our proposed approach is considerably more resilient against multiple consecutive over-the-air playbacks, corroborating its higher robustness in noisy environments.
Single-shot weakly-supervised object detection guided by empirical saliency model

Even though weakly-supervised object detection (WSOD) has become an effective method to relieve the heavy work of labeling, there are still difficult problems to be solved. WSOD method represented by a Multiple Instance Learning (MIL) have some common problems including running slowly and focusing on discriminative parts rather than the whole object, which will lead to false detection. To improve the efficiency and accuracy, we propose a single-shot weakly-supervised object detection model guided by empirical saliency model (SSWOD). As human vision always focuses on the most attracting parts of the image, saliency maps can usually guide our model to locate the most promising object areas. By this way, our model takes the saliency areas as pseudo ground-truths to realize the WSOD task with only class labels. Moreover, empirical saliency is designed to refine the pseudo ground-truth and improve the detection. Our new framework not only realizes a one-step detection without region proposals, but also reduces computational consumption. Experiments on PASCAL VOC 2007 & 2012 benchmarks demonstrate that SSWOD is 8 times faster and 5 times smaller than previous approaches, surpassing the state-of-the-art WSOD methods by 6.1% mean average precision (mAP).
Extractive Text Summarization for Thai Travel News Based on Keyword Scored in Thai Language

In recent years, people are seeking for a solution to improve text summarization for Thai language. Although several solutions such as PageRank, Graph Rank, Latent Semantic Analysis (LSA) models, etc., have been proposed, research results in Thai text summarization were restricted due to limited corpus in Thai language with complex grammar. This paper applied a text summarization system for Thai travel news based on keyword scored in Thai language by extracting the most relevant sentences from the original document. We compared LSA and Non-negative Matrix Factorization (NMF) to find the algorithm that is suitable with Thai travel news. The suitable compression rates for Generic Sentence Relevance score (GRS) and K-means clustering were also evaluated. From these experiments, we concluded that keyword scored calculation by LSA with sentence selection by GRS is the best algorithm for summarizing Thai Travel News, compared with human with the best compression rate of 20%.
Understanding Brand Authenticity Sentiments using Big Data Analytics

Social media is the major place where people share their feelings in reviews and opinions about anything such as events, products, people, article and many more. These sentiments have great influence on readers who read those opinions and construct their own opinions in the matters. The unstructured data collected from varied sources needs to be analyzed and well-structured to address specific market questions and for this purpose sentiment analysis has received considerable attention over the years. Sentiment analysis is used to decipher the mind-set or feelings of a writer from his review. Many scholars have discussed the ability of deep learning models to resolve the question of knowing consumer emotions. In this paper, we are interested in handling the sentiment analysis task using various deep learning models such as Simple Neural Network, Convolutional Neural Network (CNN) and two Recurrent Neural Networks (RNN) -Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU).
"To Tell You the Truth" by Interval-Private Data

We present a new concept of privacy and corresponding mechanisms for privatizing data that will be collected for further learning. The privacy, named as Interval Privacy, enforces the distribution of the raw data conditional on privatized data to be the same as its unconditional distribution over a nontrivial support set. The proposed privatizing mechanism is based on interval censoring techniques, where a set of points is recorded as a set of random intervals containing them. We study some theoretical properties of the proposed privacy mechanism. We demonstrate its use with various examples. Particularly, in the context of supervised regression, we develop a general method that can adapt existing regression algorithms to address interval-valued data.