Reviewerly/exHarmony huggingface dataset:
An Improved Class-Center Method for Text Classification Using Dependencies and WordNet

Automatic text classification is a research focus and core technology in natural language processing and information retrieval. The class-center vector method is an important text classification method, which has the advantages of less calculation and high efficiency. However, the traditional class-center vector method for text classification has the disadvantages that the class vector is large and sparse; its classification accuracy is not high and it lacks semantic information. To overcome these problems, this paper proposes an improved class-center method for text classification using dependencies and the WordNet dictionary. Experiments show that, compared with traditional text classification algorithms, the improved class-center vector method has lower time complexity and higher accuracy on a large corpus.
Linear Programming Bounds for Cardinality and Energy of Codes of Given Min and Max Distances

We employ signed measures that are positive definite up to certain degrees to establish Levenshtein-type upper bounds on the cardinality of codes with given minimum and maximum distance, and universal lower bounds on the potential energy (for absolutely monotone interactions) for codes with given maximum distance and fixed cardinality. In particular, we extend the framework of Levenshtein bounds for such codes.
Decoding Gabidulin Codes via Partial Inverses of Linearized Polynomials

We study Gabidulin codes from a partial-inverse perspective and obtain a key equation with a new converse, as well as a new interpolation formula. The resulting new algorithm is efficient and conceptually simple.
Searching for Highly Nonlinear DPA-Resistant Balanced Boolean Functions in the Rotation Symmetric Class

Transparency order, proposed by Prouff in 2005 and redefined by Chakraborty et al. in 2017, is a property to assess the resistance of a Boolean function or an S-box to Differential Power Analysis (DPA) attacks. In this paper, we develop an efficient algorithm based on the Gradient Descent algorithm, and search for balanced functions with good transparency order and high nonlinearity in the rotation symmetric Boolean functions (RSBFs) class. As a result, we find some cryptographically significant Boolean functions whose resistance to DPA attacks is much better than others obtained previously.
Procedure for Identifying Odd-sized Nucleotide Sequences as Codewords of BCH Codes over GF(4)

In order to understand how biological information within DNA and RNA biosignals is preserved, several works have proposed that these biosequences can be identified as codewords of BCH error correcting codes over GF(4) (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ). The current mathematical tool, used to perform such identification, has some limitations: one of them being the need of knowing all binary primitive polynomials and the other one is essentially due to the constraint imposed by the cyclic code construction leading to a restriction regarding the nucleotide sequence lengths. In this work, a novel algorithm for identifying odd-length nucleotide sequences as codewords of BCH codes over GF(4) is presented. As a result more than 270 cDNA sequences of nine different lengths, which could not be considered by the previous algorithm, are identified as codewords of 35 BCH codes.
On the differences between <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e240" altimg="si4.svg"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math>Boosting and the Lasso

In this short note, we prove that L2Boosting lacks a theoretical property which is central to the behavior of ℓ1-penalized methods such as basis pursuit and the Lasso.
Peanuts minus Schulz: Distributed Labor as a Compositional Practice

This article reflects on the future of comics in an interconnected globalized world where, it is argued, digital technologies both accelerate change in partly-uncharted territories, and redefine the contemporary disenchantment with information flows. As a case study, the author uses their project <em>Peanuts minus Schulz</em> and discusses the ethos of post-digital conceptual comics and how distributed digital labor is used as an opaque, material and possibly disruptive compositional practice.
Non-monotonic Reasoning for Scenario Awareness over Emergency Knowledge Base

Earthquakes are happening in a regular manner which effects human life and assets. Hundreds of people have been killed since the start of the year as a result of earthquakes. Therefore it shows that planning is required. Planning will be better if Decision Makers have good knowledge of the past incidents of emergencies which can be found only with the help of good reasoning. In this paper, we provide non-monotonic reasoning over emergency knowledge base, which help the decision maker to take best decision in the absence of complete information. All necessary information have been stored into knowledge base in form of resource description framework and represented in the right way. With this system, anyone in the society will be aware the scenario of past incidents and predict the damages of incoming earthquake incidents. The main aim is to reduce the human effort for getting the information of earthquake and provides real time scenario awareness to the public.
The World from Kabul: Internet Censorship in Afghanistan

This study reports on the policy, mechanism, and location of Internet censorship in a small but important country, Afghanistan. Our first contribution is to show that Afghanistan is in fact far more censorious than previously thought. Secondly, we note that most of the censorship is physically located outside the country. We expected that this might be because Afghanistan receives its Internet connectivity from providers in Iran, Pakistan, China, and other highly censorious countries. To our surprise, we find the filters are actually located in upstream nations that are not known to be censorious, notably Germany and the UK.
Image Based Search Engine - Like Using Color and Shape Features

Content Based Image Retrieval method used to match an image query with the existing image in the database and or the image on the internet. Similarity measurements are performed using the Euclidean distance function. The image to be used is an image with JPEG format. The use of image or image feature in searching for image in a database and or internet cannot be avoided, this is because searching the image by using keyword or text is very biased and the result is far from the expectation. Search engine-like used to monitor the numbers and detect the presence of new types of fauna and flora in Indonesia. Image searching was carried out by using shape features. Search engine-like is expected also to be developed into image based search engine using CBIR method. In this work we used not less than 5,000 flora and fauna images. From the experiments, it can be concluded that the effectiveness of image retrieval is quite good in term of precision and recall.
Dimensional Reduction in Behavioral Biometrics Authentication System

Authentication plays important role in digital security. It confirms the identity of the user to access the application or the system. One of the ways that used for authentication system is the user’s biometric. Every user will be identified by their biometric to determine the authentication of the user. User’s biometrics are categorized as psychological biometric and behavioral biometric. One of user’s behavior biometric that can be captured is the ways the user plays the mouse. Therefore, in this study we will use the user’s behavioral using mouse to identify their identity for the authentication system. Machine learning can be used to identify the behavior of the user by the data. Although it’s not easy work because besides of accuracy, one of the important things in the authentication system is how long the system identifies the user. The huge of the dimension of the data becomes a problem because it makes the authentication process gets slower. Hence, in this work, we propose PCA (Principal Component Analysis) for dimensional reduction. Then, SVM (Support Vector Machine) is used to model the data so that the system can identify the user by the model that be built. PCA has reduced the authentications time to 50%.
An Efficient Constraint-Based Local Search for Maximizing Water Retention on Magic Squares

Water retention on magic square is a very recently introduced variation of magic square problem which has attracted a lot of attention from the research community over the years. Different approaches have been used to tackle this highly combinatorial optimization problem. In this paper, we have proposed an improved constraint-based local search approach that remedies all the identified shortcomings of the state-of-the-art technique. Several new methods such as greediness reduction technique, incremental calculation of the objective function and solving cycling problem through similarity checking have been incorporated within the proposed local search approach. Experimental results show that our proposed algorithm performs better than the existing technique with regards to solution quality and execution time.
Non-deterministic Paillier Endorsement Asymmetric Key Cryptosystem-Based Whirlpool Hashing Quotient Filter for Secured Data Access on Cloud Storage

A non-deterministic Paillier endorsement asymmetric key cryptosystem-based Whirlpool hashing quotient filter (NPEAKC-WHQF) model is proposed with aim of securing cloud storage and data access with lower space complexity. Initially, the user registers his/her detail to cloud server (CS). After registering, the CS generates endorsement public key and private key. Then, cloud user (CU) encrypts data with their endorsement public key and sends to CS. CS stores ciphertext of data in Whirlpool hashing quotient filter (WHQF) with minimal memory space. During data access, CU sends request message. After receiving the request, CS checks endorsement public key of CU and allows only authorized user to access data. After verifying endorsement public key, user decrypts data with their endorsement private key. This helps for NPEAKC-WHQF model to improve data access security. NPEAKC-WHQF model conducts an experiment using factors, namely space complexity, data confidentiality rate, and authentication accuracy with respect to diverse number of user and data.
A Novel Semantic Inference Model With a Hierarchical Act Labels Embedded for Dialogue Act Recognition

As an important component in dialogue system, Dialogue Act Recognition (DAR) has attracted much attention of researchers. Most of the existing approaches only focus on the information of utterances, but relatively few investigate the roles of DA labels in different layers of a conversation and also some semantic inference in irregular conversations, such as questions and answers skipping back and forth, has not been probed properly so that some dialogue acts are not always recognized correctly. To overcome the aforementioned deficiencies, we propose a hierarchical project method, in which DA labels are embedded in three levels (word, utterance, and DA) to calculate the importance of each word, provide contextual and pragmatic information, and guide the model to generate the category of utterance, respectively. Both multiple attention mechanism and semantic update mechanism are applied to infer the more accurate representation of utterance for DA recognition in a general case. Experimental results show that our model can achieve comparable performance to the state-of-the-art models with less extra information.
Documentation as a cross-cutting concern of software

Traditionally, software documentation is either embedded directly into the source code as comments and therefore tightly coupled to it or maintained separately and completely divorced from the code. This work explores an alternative mechanism for writing, storing, and maintaining software documentation where comment points derived from revision control artifacts associated with the software are embedded in source code. These comment points abstractly link to software documentation, which is stored separately from the source code but remains maintainable from the source code using traditional software development tools and workflows, or using documentation focused tools like wikis. By treating documentation as a cross-cutting concern of source code, we intend to increase the modularity of documentation. This, in turn, allows different documentation concerns to be supported without mixing those concerns. Examples of such concerns include documentation associated with the architectural design, articulated requirements for the software, a literate description of algorithms, external developer API documentation, accessibility of international documentation, and pedagogical exposition.
Role of Activation Functions and Order of Input Sequences in Question Answering

This paper describes a solution for the Question Answering problem in Natural Language Processing using LSTMs. We perform an analysis on the effect of choice of activation functions in the final layer of LSTM cell on the accuracy. Facebook Research’s bAbI dataset is used for our experiments. We also propose an alternative solution, which exploits the language structure and order of words in the English language, i.e. reversing the order of paragraph will introduce many short-term dependencies between the textual data and the initial tokens of a question. This method improves the accuracy in more than half of the tasks by more than 30% over the current state of the art. Our contributions in this paper are improving the accuracy of most of the Q&A tasks by reversing the order of words in the query and the story sections. Also, we have provided a comparison of different activation functions and their respective accuracies with respect to all the 20 different NLP tasks.
Government and Public Sector

Blockchain can help government activities across multiple use case categories as a static store of secure information or a dynamic store of tradable information. One area in which blockchain can help government is record management. National, state, and local governments are in charge of keeping up people’s records, such as birth, passing dates, and property exchanges. Some of these records still exist in paper form. Modifying and updating these records is tedious, superfluous, and frustrating. Blockchain technology can rearrange the record-keeping and make the records more secure. Marriage, death, and birth certificates could be stored in the blockchain network, where one’s data will be recovered safely. Decentralized file storage, where data is distributed throughout the network, protects files from getting hacked or lost. Many local, city, and state governments have shown interest in storing and managing government records on blockchain. For example, the states of Vermont and Delaware, as well as the city of Dubai, are planning to use blockchain technology for government records including property ownership records, utility bills, and permits (Van der Meulen 2018).
Fall Guard

In order to solve the situation that the elderly can't get timely assistance when they fall down, this paper designs and implements an Android-based fall monitoring application for the elderly (Fall Guard). Combined with existing fall detection devices and cloud server, Fall Guard uses Model-View-Control (MVC) structure and OkHttp network request framework. In this paper, OkHttp is used to send network requests to the cloud server and the fall detection device is bound to the mobile client to obtain user and device information. After successfully obtaining data, the obtained information is displayed on the user interface through JSON parsing, including device positioning, electronic fence, fall alarm information and motion track. One login account can be bound to multiple devices. The login account is set to the emergency contact number of the elderly by default. When the elderly fall, Fall Guard can receive many types of fall alarm prompts, including alarm information list, notification bar reminder, SMS notification, device user status bar information and other prompt functions. Test results show that Fall Guard has good monitoring accuracy and terminal compatibility. On the one hand, it can adapt to different models and brands of Android mobile terminals to achieve accurate positioning and alarm functions. On the other hand, due to its one-to-many management mode, it can be applied to deployment of different application scenarios such as home, community and nursing home.
Crossing boundaries with research tools

This experience report discusses the value of a commercially available mobile ethnography tool used by customer experience researchers in the tourism and retail industries for research in technical communication. It reports on a test study conducted called "Day in the Life" with 5 participants working in technical communication, UX Design, and related fields. Four areas of concern are explored: usability of tool for participants, what the tool can and cannot measure, study designs supported by the tool, and securing IRB approval. Overall, this report discusses whether the tool gathers the right kind of contextual data for technical communication research.
Deep Multi-task Learning with Cross Connected Layer for Slot Filling

Slot filling is a critical subtask of Spoken language understanding (SLU) in task-oriented dialogue systems. This is a common scenario that different slot filling tasks from different but similar domains have overlapped sets of slots (shared slots). In this paper, we propose an effective deep multi-task learning with Cross Connected Layer (CCL) to capture this information. The experiments show that our proposed model outperforms some mainstream baselines on the Chinese E-commerce datasets. The significant improvement in the F1 socre of the shared slots proves that CCL can capture more information about shared slots.
SIDEKICK: Linear Correlation Clustering with Supervised Background Knowledge

While explainable AI (XAI) is gaining in popularity, other more traditional machine learning algorithms can also benefit from increased explainability. A semi-supervised approach to correlation clustering opens up a promising design space that might provide such explainability to correlation clustering algorithms. In this work, semi-supervised linear correlation clustering is defined as the task of finding arbitrary oriented subspace clusters using only a small sample of supervised background knowledge provided by a domain experts. This work describes a first foray into this novel approach and provides an implementation of a basic algorithm to perform this task. We have found that even a small amount of supervised background knowledge can significantly improve the quality of correlation clustering in general. With confidence it can be stated, the results of this work have the potential to inspire several more semi-supervised approaches to correlation clustering in the future.
Resources’ Planning for Cloud Computing Using Different Types of Regression Analysis

Prediction of resources requirement of CPU and memories is very important to improve the performances of data in cloud environment. Resource planning is the greatest challenge due to the fluctuation of demand of cloud resources; we face over-and-under utilization of many resources. We will be benefited if we know or predict the future load information in advance. For considering, we can use regression techniques to predict whether the available resources meet the feature requirement.
Comparison of Different Task Scheduling Algorithms in Cloud Computing Environment Using Cloud Reports

Scheduling is quality-of-service parameters over Cloud data centers. As the scheduler is often executed, it keeps all computer resources busy as in some cases, i.e., load balancing. Hence, it allows numerous users to share system resources efficiently, to attain the desired quality of service. Here, we have implemented three different schedulers, i.e., Time-Shared scheduling, Space-Shared scheduling, and Dynamic-Workload scheduling over a Cloud data center. During each phase of computation, we have shown the resource utilization (in terms of CPU, RAM, and bandwidth), cloudlets for virtual machine and execution time.
Basic Ideas of Information Thermodynamics

We apply a certain unifying physical description of the results of Information Theory. Assuming that heat entropy is a thermodynamic realization of information entropy, we construct a cyclical, thermodynamic, average-value model of an information transfer chain as a general heat engine, in particular a Carnot engine, reversible or irreversible. A working medium of the cycle (a thermodynamic system transforming input heat energy) can be considered as a thermodynamic, average-value model or, as such, as a realization of an information transfer channel. We show that for a model realized in this way the extended II. Principle of Thermodynamics is valid and we formulate its information form.In addition we solve the problem of a proof of II. Principle of Thermodynamics. We state the relation between the term of information entropy, introduced by C. Shannon (1948), and thermodynamic entropy, introduced by R. Clausius (1850) and, further, explain the Gibbs paradox. Our way to deal with the given topic is a connection of both the mathematical definitions of information entropies and their mutual relations within a system of stochastic quantities, especially with thermodynamic entropies defined on an isolated system in which a realization of our (repeatable) observation is performed [it is a (cyclic) transformation of heat energy of an observed, measured system].We use the information description to analyze the Gibbs paradox, reasoning it as a property of such observation, measuring an (equilibrium) thermodynamic system. We state a logical proof of the II. P.T. as a derivation of relations among the entropies of a system of stochastic variables, realized physically, and, the Equivalence Principle of the I., II. and III. Principle of Thermodynamics is formulated.
Dynamic Obstacle Detection in Traffic Environments

The research on autonomous vehicles has grown increasingly with the advent of neural networks. Dynamic obstacle detection is a fundamental step for self-driving vehicles in traffic environments. This paper presents a comparison of state-of-art object detection techniques like Faster R-CNN, YOLO and SSD with 2D image data. The algorithms for detection in driving, must be reliable, robust and should have a real time performance. The three methods are trained and tested on PASCAL VOC 2007 and 2012 datasets and both qualitative and quantitative results are presented. SSD model can be seen as a tradeoff for speed and small object detection. A novel method for object detection using 3D data (RGB and depth) is proposed. The proposed model incorporates two stage architecture modality for RGB and depth processing and later fused hierarchically. The model will be trained and tested on RGBD dataset in the future.
Demo Abstract: Explaining Web users' QoE with Factorization Machines

Whereas most of the literature employs classic machine learning techniques (such as C4.5 trees, Random Forest and Support Vector Machines) to improve forecast accuracy of QoE models, in this demo we explore the use of an information filtering system (Factorization Machine) to get fundamental insights and explain the relationship between QoE and different features.
PROF: An Assistive Tool for Educators to Semi-Autonomously Create Topic Maps and Skill Trees

In this lightning-talk paper, we discuss the Pedagogical Resource Organization Framework (PROF), a new process which automatically creates Topic Maps and Skill Trees from unorganized data sources. The goal of this tool is to identify learning paths from unstructured data.