Reviewerly/exHarmony huggingface dataset:
New Problem Transformation Method Based on the Local Positive Pairwise Dependencies Among Labels

Multi-label classification (MLC) generalises the conventional binary and multi-class classification by allowing instances to be linked with one or more of the class labels. Therefore, class labels in MLC are not mutual exclusive as in single label classification (SLC). Consequently, the search space of the MLC problem is large compared with that of SLC and grows in an exponential way. One main approach to solve MLC problem is through forcing instances to be associated with only one class label. This approach of handling the problem of MLC has been widely known as problem transformation method (PTM). Existing PTMs depend on the frequency of class labels as a transformation criterion, which causes several problems such as imbalance class distribution, complicating the training phase and most importantly decreasing the accuracy of the classification task. Therefore, in this paper, a new PTM is proposed based on the positive local dependencies among labels. The proposed PTM aims to facilitate capturing the most accurate positive dependencies among labels and hence improve the predictive performance of the classification task. Experiments on several datasets revealed the superiority of the proposed PTM compared with the existing PTMs, especially with high cardinality datasets.
Gesture of slow learner student in mathematical Communication

Abstract This article discusses the gesture of slow learner students in solving the problem of mathematical communication. Gesture aims to understand and deepen the thinking process of students who have difficulty conveying ideas either with oral or written. This research is a qualitative research. Data were collected by giving two items of mathematical communication test to a fourth-grade slow learner student of elementary school, then the student gesture was observed through video recording which was obtained when students did the question. The results of this study reveal the pattern of slow learner gesture, i.e.: Iconic gesture: using the fingers and toes in counting. Representational gesture: the student could not answer a question with a clear rational, student was less able to explain her ideas, but did not feel embarrassed to ask. Writing gesture: student could write her ideas in writing after a given direction, but she still made a mistake in writing the mathematical symbols (like numbers) and forgot to write arithmetic operations (addition operation). For further research it is necessary to look at gestures with a greater number of samples and variations, both in terms of gender, grade, or cases of other children with Special Needs.
Adaptive Probabilistic Word Embedding

Word embeddings have been widely used and proven to be effective in many natural language processing and text modeling tasks. It is obvious that one ambiguous word could have very different semantics in various contexts, which is called polysemy. Most existing works aim at generating only one single embedding for each word while a few works build a limited number of embeddings to present different meanings for each word. However, it is hard to determine the exact number of senses for each word as the word meaning is dependent on contexts. To address this problem, we propose a novel Adaptive Probabilistic Word Embedding (APWE) model, where the word polysemy is defined over a latent interpretable semantic space. Specifically, at first each word is represented by an embedding in the latent semantic space and then based on the proposed APWE model, the word embedding can be adaptively adjusted and updated based on different contexts to obtain the tailored word embedding. Empirical comparisons with state-of-the-art models demonstrate the superiority of the proposed APWE model.
Type Inference for C

Type inference is a feature that is common to a variety of programming languages. While, in the past, it has been prominently present in functional ones (e.g., ML and Haskell), today, many object-oriented/multi-paradigm languages such as C# and C++ offer, to a certain extent, such a feature. Nevertheless, type inference still is an unexplored subject in the realm of C. In particular, it remains open whether it is possible to devise a technique that encompasses the idiosyncrasies of this language. The first difficulty encountered when tackling this problem is that parsing C requires, not only syntactic, but also semantic information. Yet, greater challenges emerge due to C’s intricate type system. In this work, we present a unification-based framework that lets us infer the missing struct, union, enum, and typedef declarations in a program. As an application of our technique, we investigate the reconstruction of partial programs. Incomplete source code naturally appears in software development: during design and while evolving, testing, and analyzing programs; therefore, understanding it is a valuable asset. With a reconstructed well-typed program, one can: (i) enable static analysis tools in scenarios where components are absent; (ii) improve precision of “zero setup” static analysis tools; (iii) apply stub generators, symbolic executors, and testing tools on code snippets; and (iv) provide engineers with an assortment of compilable benchmarks for performance and correctness validation. We evaluate our technique on code from a variety of C libraries, including GNU’s Coreutils and on snippets from popular projects such as CPython, FreeBSD, and Git.
An Elderly First Aid System Based-Fall Detection and Unmanned Aerial Vehicle

Abstract A first-aid system is proposed for monitoring elderly patients who risk falling based on fall detection system (FDs) and for providing the first aid kit from an emergency call centre (ECC) to them by using an unmanned aerial vehicle (UAV). In previous studies, the measurement accuracy of falls and heart rate was not sufficiently accurate. In addition, determination of UAV delivery time is also crucial and can save lives. In this work, the proposed system aims to determine patient falls and accurately measure heart rate and reduce UAV delivery time to patients. The FDs was practically implemented based on heartbeat and accelerometer sensors, a microcontroller, a GSM module, and GPS. While, a UAV, a first aid kit, and Smartphone were adopted in an ECC. Falls were accurately detected based on elderly fall detection (EFD) algorithm. The result showed that the proposed FDs was succeeded in monitoring elderly patients’ vital signs with a fall detection accuracy of 99.11%. In addition, the UAV succeeded in all missions and arrived at the patient’s locations before the ambulance in urban areas, with an average time saving of 1.75 min. The proposed elderly first aid system outperformed previous systems presented in terms of fall detection and UAV arrival time.
Dynamic stochastic approximation for multi-stage stochastic optimization

In this paper, we consider multi-stage stochastic optimization problems with convex objectives and conic constraints at each stage. We present a new stochastic first-order method, namely the dynamic stochastic approximation (DSA) algorithm, for solving these types of stochastic optimization problems. We show that DSA can achieve an optimal $${{\mathcal {O}}}(1/\epsilon ^4)$$ rate of convergence in terms of the total number of required scenarios when applied to a three-stage stochastic optimization problem. We further show that this rate of convergence can be improved to $${{\mathcal {O}}}(1/\epsilon ^2)$$ when the objective function is strongly convex. We also discuss variants of DSA for solving more general multi-stage stochastic optimization problems with the number of stages $$T > 3$$ . The developed DSA algorithms only need to go through the scenario tree once in order to compute an $$\epsilon $$ -solution of the multi-stage stochastic optimization problem. As a result, the memory required by DSA only grows linearly with respect to the number of stages. To the best of our knowledge, this is the first time that stochastic approximation type methods are generalized for multi-stage stochastic optimization with $$T \ge 3$$ .
New internal metric for software clustering algorithms validity

Clustering (modularisation) techniques are often employed for the meaningful decomposition of a program aiming to understand it. In the software clustering context, several external metrics are presented to evaluate and validate the resultant clustering obtained by an algorithm. These metrics use a ground-truth decomposition to evaluate a resultant clustering. When there exists no ground-truth decomposition for a software system, internal metrics are utilised to validate clustering algorithms. Due to the comparison with a reference decomposition, external metrics are preferred to internal metrics. Available internal metrics used to measure the clustering quality are not appropriate for evaluating because they do not consider the purpose of software clustering, which is to understand a software system. In this study, the authors present six criteria that influence the understanding of a program. Then the authors design an internal metric for estimating the software clustering quality considering those criteria. They selected ten folders of Mozilla Firefox with different sizes and functionalities to assess the reliability of the proposed metric. The experimental results confirm that the proposed internal metric is more accurate than the existing internal metrics in terms of proximity to expert decomposition. The proposed internal metric can be a substitute for external metrics.
Fast Generating A Large Number of Gumbel-Max Variables

The well-known Gumbel-Max Trick for sampling elements from a categorical distribution (or more generally a nonnegative vector) and its variants have been widely used in areas such as machine learning and information retrieval. To sample a random element i (or a Gumbel-Max variable i) in proportion to its positive weight vi, the Gumbel-Max Trick first computes a Gumbel random variable gi for each positive weight element i, and then samples the element i with the largest value of gi + ln vi. Recently, applications including similarity estimation and graph embedding require to generate k independent Gumbel-Max variables from high dimensional vectors. However, it is computationally expensive for a large k (e.g., hundreds or even thousands) when using the traditional Gumbel-Max Trick. To solve this problem, we propose a novel algorithm, FastGM, that reduces the time complexity from O(kn+) to O(kln k + n+), where n+ is the number of positive elements in the vector of interest. Instead of computing k independent Gumbel random variables directly, we find that there exists a technique to generate these variables in descending order. Using this technique, our method FastGM computes variables gi + ln vi for all positive elements i in descending order. As a result, FastGM significantly reduces the computation time because we can stop the procedure of Gumbel random variables computing for many elements especially for those with small weights. Experiments on a variety of real-world datasets show that FastGM is orders of magnitude faster than state-of-the-art methods without sacrificing accuracy and incurring additional expenses.
I’ve Got Your Packages: Harvesting Customers’ Delivery Order Information using Package Tracking Number Enumeration Attacks

A package tracking number (PTN) is widely used to monitor and track a shipment. Through the lenses of security and privacy, however, a package tracking number can possibly reveal certain personal information, leading to security and privacy breaches. In this work, we examine the privacy issues associated with online package tracking systems used in the top three most popular package delivery service providers (FedEx, DHL, and UPS) in the world and found that those websites inadvertently leak users' personal data with a PTN. Moreover, we discovered that PTNs are highly structured and predictable. Therefore, customers' personal data can be massively collected via PTN enumeration attacks. We analyzed more than one million package tracking records obtained from Fedex, DHL, and UPS, and showed that within 5 attempts, an attacker can efficiently guess more than 90% of PTNs for FedEx and DHL, and close to 50% of PTNs for UPS. In addition, we present two practical attack scenarios: 1) to infer business transactions information and 2) to uniquely identify recipients. Also, we found that more than 109 recipients can be uniquely identified with less than 10 comparisons by linking the PTN information with the online people search service, Whitepages.
Natural Language Annotations for Search Engine Optimization

Understanding content at scale is a difficult but important problem for many platforms. Many previous studies focus on content understanding to optimize engagement with existing users. However, little work studies how to leverage better content understanding to attract new users. In this work, we build a framework for generating natural language content annotations and show how they can be used for search engine optimization. The proposed framework relies on an XGBoost model that labels “pins” with high probability phrases, and a logistic regression layer that learns to rank aggregated annotations for groups of content. The pipeline identifies keywords that are descriptive and contextually meaningful. We perform a large-scale production experiment deployed on the Pinterest platform and show that natural language annotations cause a 1-2% increase in traffic from leading search engines. This increase is statistically significant. Finally, we explore and interpret the characteristics of our annotations framework.
Initial design of blended learning for mathematics subject using the Kelase platform by adopting content of Tri Kaya Parisudha

Abstract Currently, e-learning is suitable to be used as one of the learning models in Mathematics subject on High School or Vocational School level as an impact of the presence of the 4.0 industrial revolution. However, this e-learning model is not always effective to be used to obtain quality learning outcomes because it can only be used to measure cognitive domains. To obtain learning outcomes in the cognitive, affective, and psychomotor domains, so another learning model innovation is needed. One model that can be used is blended learning for Mathematics subject using the Kelase platform with material content that is packaged based on the Tri Kaya Parisudha concept. The Kelase platform is used to create blended learning facilities that contain features needed to accommodate material content. The Tri Kaya Parisudha concept, which consists of manacika, wacika, and kayika, is used as the basic foundation for making material content that can measure cognitive, affective, and psychomotor domains. Based on those statements, the purpose of this study was to find out the general description of the initial design of blended learning for Mathematics subject (especially at the high school or vocational school level) that was created using the Kelase platform with material content based on Tri Kaya Parisudha . The method used in this study was R&amp;D with the Borg and Gall model, which focuses on the stages of design development. The subjects involved in designing this blended learning were three people. The tools used to make blended learning designs, such as Kelase platform and Balsamiq Mockups . The results obtained in this study were the formation of an initial design of blended learning that created used Kelase platform by presenting facilities that could be used to fill Tri Kaya Parisudha -based Mathematics content.
Wearable Computing for Dementia Patients

The number of dementia patients is increasing dramatically. As the dementia cannot be cured, only drug treatment can temporarily improve the symptoms. The doctor needs to adjust the medication according to the patients’ symptoms. Therefore, the caregiver is required to give information about the patients’ behavioral and psychological symptoms thoroughly in order for the doctor to adjust the medication efficiently. However, the caregiver has many daily tasks to complete. Thus, he might not know the behavioral and psychological symptoms in detail. This research proposes a wearable computing that monitor patient activities. This research is done based on the user centered design. Therefore, after the interview with doctors and caregivers, we finalized the activities that the doctors need to know in order to adjust the medication efficiently. The activities are stand-sit, shaking, walking, sitting and standing. Moreover, the best position of the wearable was concluded to be on the back of the patients. Then according to the previous works in fall detection systems, the models that we chose to compare their performance are feedforward neural network, support vector machine, decision tree and random forest. The selected features are Mean, Standard Deviation (STD), Peak counts, Zero crossing rate (ZCR), Spectral Energy and Spectral Entropy of x, y, z axis data from accelerometer and gyroscope. The result shows that the feedforward neural network achieved the highest accuracy.
A Generic Edge-Empowered Graph Convolutional Network via Node-Edge Mutual Enhancement

Graph Convolutional Networks (GCNs) have shown to be a powerful tool for analyzing graph-structured data. Most of previous GCN methods focus on learning a good node representation by aggregating the representations of neighboring nodes, whereas largely ignoring the edge information. Although few recent methods have been proposed to integrate edge attributes into GCNs to initialize edge embeddings, these methods do not work when edge attributes are (partially) unavailable. Can we develop a generic edge-empowered framework to exploit node-edge enhancement, regardless of the availability of edge attributes? In this paper, we propose a novel framework EE-GCN that achieves node-edge enhancement. In particular, the framework EE-GCN includes three key components: (i) Initialization: this step is to initialize the embeddings of both nodes and edges. Unlike node embedding initialization, we propose a line graph-based method to initialize the embedding of edges regardless of edge attributes. (ii) Feature space alignment: we propose a translation-based mapping method to align edge embedding with node embedding space, and the objective function is penalized by a translation loss when both spaces are not aligned. (iii) Node-edge mutually enhanced updating: node embedding is updated by aggregating embedding of neighboring nodes and associated edges, while edge embedding is updated by the embedding of associated nodes and itself. Through the above improvements, our framework provides a generic strategy for all of the spatial-based GCNs to allow edges to participate in embedding computation and exploit node-edge mutual enhancement. Finally, we present extensive experimental results to validate the improved performances of our method in terms of node classification, link prediction, and graph classification.
The Chameleon Attack: Manipulating Content Display in Online Social Media

Online social networks (OSNs) are ubiquitous attracting millions of users all over the world. Being a popular communication media OSNs are exploited in a variety of cyber-attacks. In this article, we discuss the chameleon attack technique, a new type of OSN-based trickery where malicious posts and profiles change the way they are displayed to OSN users to conceal themselves before the attack or avoid detection. Using this technique, adversaries can, for example, avoid censorship by concealing true content when it is about to be inspected; acquire social capital to promote new content while piggybacking a trending one; cause embarrassment and serious reputation damage by tricking a victim to like, retweet, or comment a message that he wouldn’t normally do without any indication for the trickery within the OSN. An experiment performed with closed Facebook groups of sports fans shows that (1) chameleon pages can pass by the moderation filters by changing the way their posts are displayed and (2) moderators do not distinguish between regular and chameleon pages. We list the OSN weaknesses that facilitate the chameleon attack and propose a set of mitigation guidelines.
Development and Design Strategies of Evidence Collection Framework in Cloud Environment

Nowadays, cloud computing is one of the popular and widely used concepts in information technology paradigm. It is committed to improving the IT business technically and economically. On the other hand, digital forensic is the process of collection, identification, preservation, examination, and analysis of data or information for the proof in the court of law as an evidence. It is very difficult and challenging to apply digital forensic operation in a cloud environment because CSPs are dependent on each other either they provide IaaS, PaaS, or SaaS. So the cloud forensic, one of the applications of digital forensic in a cloud environment, is just a subset of network forensic. It is a cross-field of digital forensic and cloud computing. In this paper, we investigate all the research issues, problems, and implementation ethics of cloud forensic from the initial level. We found that lots of issues and challenges are remaining to address in this domain. Some major research domains are architectures, data collection and analysis, anti-forensic, incident first responders, roles and responsibilities, legal, standards, and some learning issues. In our research work, we mainly focus on the data collection and cloud forensic architectures and also implement a cloud forensic framework in the context of cloud service models. This research work is tested using different private cloud solutions such as eucalyptus, OpenNebula, VMware, vCloud, and Hadoop platform. In our research work, we implement pattern search facility using the proposed approach in open-source software called digital forensic framework. We also implement in near future digital forensic triage using Amazon Elastic MapReduce. In this research, we also implement designed and development of forensic method for the PaaS and SaaS delivery models of cloud computing, also apply machine learning principles to design and develop new digital forensic methods, and improve the efficiency of investigation using machine learning algorithms for feature extraction and priority of evidence classification of evidence in virtual machines.
Adjustable Contrast Enhancement Using Fast Piecewise Linear Histogram Equalization

Histogram equalization is a technique to enhance the contrast of the image by redistributing the histogram. In this paper, a fast piecewise linear histogram equalization method is introduced based on an adjustable degree of enhancement and piecewise continuous transformation functions using frequencies of different grey-levels. This method aims to address and maximize the contrast enhancement of the image by stretching the entire spectrum. For this purpose, particular nodes (bins) on the histogram are simultaneously detected that in comparison with recursive methods, it requires less computational time. Then, the particular nodes are stretched using transformation functions to align with the reference nodes. The experimental results indicate that the performance of the proposed method is promising in terms of contrast enhancement. Moreover, this method preserves the texture of various regions in the image very well through the equalization process by using the degree of enhancement.