Reviewerly/exHarmony huggingface dataset:
Time-Frequency Characteristics of In-Home Radio Channels Influenced by Activities of the Home Occupant

While aging is a serious global concern, in-home healthcare monitoring solutions are limited to context-aware systems and wearable sensors, which may easily be forgotten or ignored for privacy and comfort reasons. An emerging non-wearable fall detection approach is based on processing radio waves reflected off the body, who has no active interaction with the system. This paper reports on an indoor radio channel measurement campaign at 5.9 GHz, which has been conducted to study the impact of fall incidents and some daily life activities on the temporal and spectral properties of the indoor channel under both line-of-sight (LOS) and obstructed-LOS (OLOS) propagation conditions. The time-frequency characteristic of the channel has been thoroughly investigated by spectrogram analysis. Studying the instantaneous Doppler characteristics shows that the Doppler spread ignores small variations of the channel (especially under OLOS conditions), but highlights coarse ones caused by falls. The channel properties studied in this paper can be considered to be new useful metrics for the design of reliable fall detection algorithms. We share all measured data files with the community through Code Ocean. The data can be used for validating a new class of channel models aiming at the design of smart activity recognition systems via a software-based approach.
Explicit Rate-1 Non-malleable Codes for Local Tampering

This paper constructs high-rate non-malleable codes in the information-theoretic plain model against tampering functions with bounded locality. We consider $$\delta $$ -local tampering functions; namely, each output bit of the tampering function is a function of (at most) $$\delta $$ input bits. This work presents the first explicit and efficient rate-1 non-malleable code for $$\delta $$ -local tampering functions, where $$\delta =\xi \lg n$$ and $$\xi <1$$ is any positive constant. As a corollary, we construct the first explicit rate-1 non-malleable code against NC $$^0$$ tampering functions. Before our work, no explicit construction for a constant-rate non-malleable code was known even for the simplest 1-local tampering functions. Ball et al. (EUROCRYPT–2016), and Chattopadhyay and Li (STOC–2017) provided the first explicit non-malleable codes against $$\delta $$ -local tampering functions. However, these constructions are rate-0 even when the tampering functions have 1-locality. In the CRS model, Faust et al. (EUROCRYPT–2014) constructed efficient rate-1 non-malleable codes for $$\delta = O(\log n)$$ local tampering functions. Our main result is a general compiler that bootstraps a rate-0 non-malleable code against leaky input and output local tampering functions to construct a rate-1 non-malleable code against $$\xi \lg n$$ -local tampering functions, for any positive constant $$\xi < 1$$ . Our explicit construction instantiates this compiler using an appropriate encoding by Ball et al. (EUROCRYPT–2016).
Indicator diagrams of oil free scroll vacuum pumps

Summary Experimental test unit for obtaining indicator diagrams of a scroll vacuum pump was developed. Indicator diagrams for different pump inlet pressures and different rotation speeds of the drive shaft were obtained. The obtained results are analyzed.
Multi factor user authentication mechanism using internet of things

IoT (Internet of Things) has been one among the key areas of research; here huge number of things i.e. smart devices is connected over internet. Users are connected over internet with different devices using Internet of Things where the data is exchanged and processed with other devices as well as end users. client Verification is an important issue in internet-based applications. A standout amongst the most notable proof arrangements utilized right presently are the alphanumeric-based plans. To control the way users, examine graphic style pictures greater than reviewing alphanumeric secrets graphic critical solutions are offered. Clients who will choose weak passwords are exposed to lexicon attacks. For such attacks, graphical passwords provide much security. In this paper we recommend a two-stage confirmation approach mixing graphical passwords and Internet of Things.
Evolving with patterns: a 31-month startup experience report

Software startups develop innovative products under extreme conditions of uncertainty. At the same time they represent a fast-growing sector in the economy and scale up research and technological advancement. This paper describes findings after observing a startup during its first 31 months of life. The data was collected through observations, unstructured interviews as well as from technical and managerial documentation of the startup. The findings are based on a deductive analysis and summarized in 24 contextualized patterns that concern communication, interaction with customer, teamwork, and management. Furthermore, 13 lessons learned are presented with the aim of sharing experience with other startups. This industry report contributes to understanding the applicability and usefulness of startups' patterns, providing valuable knowledge for the startup software engineering community.
Providing Peripheral Trajectory Information to Avoid Motion Sickness During the In-car Reading Tasks

Carsickness is one of the most common types of motion sickness. It can be resulted by unanticipated body motion, which mismatches our anticipation of movement. However, related studies tend to increase user's visual information which instead affects the possibility of doing non-driving tasks. Therefore, matching sensory signals and engaging in the performance of non-driving tasks will be the key to improving user requirements. The experiment, which we choose to carry on a vehicle, analyses subjective ratings of motion sickness, in order that the user not only releases carsickness symptom, but also obtains a meaningful and rich traveling experience (in-vehicle reading). The result and experience shall be able to be applied in other related design research about physiological issues of future technology, such as motion sickness in autonomous driving or virtual reality.
On the use of lambda expressions in 760 open source Python projects

Lambdas as anonymous functions have gained significant prominence in programming languages such as Java, C++, Python and so on as developers tend to use them. With the dominant use of Python as backend language in many projects and large number of open source projects available, we set out to investigate the use of lambdas in Python projects and obtained 19 categories to classify lambda usages as preliminary results. Our study could help language designers to improve the state of the art libraries for lambda expressions and developers to use lambda expressions effectively.
A Concurrent Optimization Consensus System Based on Blockchain

The blockchain technology is more and more important in computer security. The core of the blockchain system is consensus. However, many consensus mechanisms reward those groups that own the majority of the resources. In addition, the performance of these consensus mechanisms is poor. In this paper we first present Proof of Value (POV), an incentive mechanism that rewards the value creators. With the help of POV, we reward the user who creates value and control the number of coins through the incentive coefficient adjustment algorithm. To improve the performance of POV in permissioned blockchain, we designed Hypernet, an off-chain transaction system. By designing the transaction system Hy-pernet, the transaction can be handled without blockchain. Finally, we evaluate our system by generating random parameters to invoke the contract. Then we record the real time for issuing the rewards. The test results show that the loss of the system is about 2% when the POV is added. The performance of the Hypernet is four times higher than the traditional permissioned blockchain transaction system.
A robust anti-occlusion object tracking method

Visual object tracking is one of the most attractive issue in computer vision. Recently, deep neural network has been widely developed in object tracking and showing great accuracy. In general, the accuracy of tracking task decreases dramatically when the background becomes complex or occluded. Thus, a robust tracking method based on convolutional neural network and anti-occlusion mechanic is presented. Benefit from the adaptive tracking confidence parameter T, the tracking effect is evaluated during tracking. Once the target is occluded, the location of the target object is corrected immediately. Experimental results demonstrate that the proposed framework achieves state-of-the-art performance on the popular OTB50 and OTB100 benchmarks.
Sketch-Based Image Retrieval via a Semi-Heterogeneous Cross-Domain Network

We propose a novel semi-heterogeneous network for sketch-based image retrieval (SBIR). By exploring different feature-extracting network structures and data augmentation algorithms, we design a high-performance deep-network based method for SBIR. Our work consists of three stages: 1) we propose a novel deep SBIR optimization model, termed a semi-heterogeneous network, to capture the cross-view similarities between different categories; 2) we develop a novel feature extraction method to find a cross-domain representation which contains the line information of sketches while retaining the original information of images; 3) we explore a more comprehensive structure though different parameter and network settings for further performance improvement. Based on our experiments on a widely used dataset, our approach significantly outperforms state-of-the-art methods.
A New Social Robot for Interactive Query-Based Summarization: Scientific Document Summarization

The extractive summartization methods try to summarize a single or multiple documents based on informative sentences exactly as they appear in source(s). One method to choose these sentences is to use users’ query, which could be problematic in many cases, specially in scientific context. One way to tackle this challenge is to gather more information about the user and his preferences. Therefore, in this paper we propose a novel framework to use the users’ feedbacks and a social robotics platform, Nao robot, has been adapted as an interacting agent. This agent has multiple communication channels and could learn the user model and adapt to his/her needs via reinforcement learning approach. The whole approach is then studied in terms of how much it is able to adapt based on user’s feedback, and also in terms of interaction time.
Adaptive Salience Preserving Pooling for Deep Convolutional Neural Networks

Pooling is an important operation in deep convolutional neural networks (CNNs). In the learning process of CNNs, the pooling layer needs to preserve feature information while significantly reduce computation amount. The conventional pooling methods, such as average pooling and max pooling cannot preserve feature information effectively in pooling layers. Therefore, a more effective and adaptive pooling mechanism is needed for pooling layers. In this paper, we propose a novel pooling mechanism to preserve feature information effectively. The novel pooling method considers the value distribution of the activations within pooling regions and operates on salient activations accordingly. This pooling method can be realized by clustering the activations in pooling regions into salient and trivial activations, and use salient activations only to compute pooling output. Compared with traditional average and max pooling methods, our pooling method is more adaptive and more likely to preserve feature information in pooling layers. Experimental results on several benchmark image datasets show that our adaptive salience preserving (ASP) pooling method outperforms existing pooling methods. Furthermore, our proposed ASP pooling can provide a "middle zone" between average pooling and max pooling. By using ASP pooling in the right stages of CNNs, we could significantly improve the network performance. For example, the performance of VGG network can be improved by changing the last two pooling layers to be ASP pooling.
Detection of DOM-Based XSS Attack on Web Application

Cross-Site Scripting (XSS) is one of the huge issues of any Web-based or Online applications. In this attack, the attacker uses malicious code to intercept the information through users web application and sends it to the corresponding web server. This is possible because web browsers are capable of executing the instructions stored in Web pages. This enables the attackers to make use of this feature, so as to execute the malicious code in a user's Web browsing application. This attack if happened, may result in very slow and poor web surfing. It is also capable of stealing the cookies, passwords and other personal information of the user. These kind of attacks are very easy in terms of implementation but the prevention or detection of this attack is a challenging task. In this paper firstly the existing research on the prevention of XSS is presented. Then a framework is proposed to detect the XSS, which can provide a legitimate solution for the mitigation of the attack.
CAPTCHA Recognition Based on Kohonen Maps

CAPTCHA is a security technology commonly used to differentiate between computers and humans. Text-based CAPTCHA is the most widely used method. This paper presents an approach based on the Kohonen maps neural network for recognizing CAPTCHA. This method first preprocesses the given CAPTCHA, segments its characters, extracts character features, and recognizes characters based on the Kohonen maps cluster analysis results. These experimental results show that the proposed method performs well in recognizing CAPTCHA.
Text Sentiment Classification Based on Layered Attention Network

The emerging attention based methods are widely used in sentiment classification, achieving the accuracy improvement of sediment classification tasks. However, these methods usually work improperly in the task of film review classification, in which positive and negative comments are often mixed and interpreting the comments from different perspectives may be diametrically opposite sentiments. In this paper, we propose a new attention based neural network architecture based on HAN model where context layer is added. Compared with the HAN, the addition of the context-aspect layer can remove the impact of unimportant sentences and improve the accuracy of sentiment classification. The experiment results on IMDB dataset show that the proposed model outperforms other existing methods, achieving an accuracy improvement of 3.11% as compared to the state-of-the-art method. The experiment results also show that our model has the better accuracy and the lower iteration time, as compared to the baseline model.
Linguistic Pattern Mining for Data Analysis in Microblog Texts using Word Embeddings

Microblog posts (e.g. tweets) often contain users opinions and thoughts about events, products, people, organizations, among other possibilities. However, the usage of social media to promote online disinformation and manipulation is not an uncommon occurrence. Analyzing the characteristics of such discourses in social media is essential for understanding and fighting such actions. Extracting recurrent fragments of text, i.e. word sequences, which are semantically similar can lead to the discovery of linguistic patterns used in certain kinds of discourse. Therefore, we aim to use such patterns to encapsulate frequent discourses textually expressed in microblog posts. In this paper, we propose to exploit linguistic patterns in the context of the 2016 United Estates presidential election. Through a technique that we call Short Semantic Pattern (SSP) mining, we were able to extract sequences of words that share a similar meaning in their word embedding representation. In the experiments we investigate the incidence of SSP instances regarding political adversaries and media in tweets posted by Donald Trump, during the presidential election campaign. Experimental results show a high preponderance of some statements of Donald Trump towards their adversaries and expressions that often appeared in such tweets.
Coretet: A Dynamic Virtual Musical Instrument for the Twenty-First Century

Coretet is a virtual reality musical instrument that explores the translation of performance gesture and mechanic from traditional bowed string instruments into an inherently non-physical implementation. Built using the Unreal Engine 4 and Pure Data, Coretet offers musicians both a flexible and articulate musical instrument to playas well as a networked performance environment capable of supporting and presenting a traditional four-member string quartet. Building on traditional stringed instrument performance practices, Coretet was designed as a futuristic `21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> Century' implementation of the core gestural and interaction modalities that generate musical sound in the violin, viola and cello. Coretet exists as a client-server software system designed to be controlled using an Oculus Rift head-mounted display (HMD) and the Oculus Touch hand-tracking controllers. The instrument and performance environment are built using the Unreal Engine 4. Gesture and audio output is generated using interaction data from the engine streamed to a Pure Data (PD) [2] server via Open Sound Control (OSC) [3]. Within PD, gestural control data from Coretet is processed and used to control a variety of audio generation and manipulation processes including the [bowed~] string physical model from the Synthesis Toolkit (STK) [1].
Keyword Search Based Mashup Construction with Guaranteed Diversity

To assist system engineers in efficiently constructing mashups, the keyword search based approach is proposed recently, which finds the optimal mashup of services with respect to QoS. However, we claim that the diversity of mashups should be taken into account due to the ambiguity of input keywords, so that the returned diverse set of mashups can improve user satisfaction by covering various possible user demands behind the keywords. For that, we present a novel keyword search based service composition approach that finds the top-k mashups dissimilar to each other. Specifically, our approach firstly searches for specific subtrees that contain all given keywords in a service connection graph as candidate mashups, and then uses an efficient graph-based algorithm to select the final diverse top-k set without evaluating the similarity between each pair of mashups. We conduct the evaluations of our approach on a real world data set crawled from the ProgramableWeb.com. The experimental results demonstrate that our approach outperforms the previous work on two selected metrics.
Interleaved Zooming Network for Image Inpainting

Recently, deep learning-based techniques have shown great potential for image inpainting tasks thanks to the powerful representation ability of convolutional neural networks. However, current approaches failed to inpaint satisfactorily in both global structures and local textures, because they only use simple dilated convolution which hard to encode comprehensive deep features. In this paper, we propose Interleaved Zooming Network to tackle this issue. The network contains two branches in encode stage, major branch maintains the size of input features, zoom-out branch convolve and downsample the features by 4 times to perceive larger visual field of input signals. These two branches communicate in an interleaved manner, that is, each branch receive signals from previous layers in both branches. We empirically demonstrate that this interleaved zooming structure can help to generate better global structures and local details of missing regions of images. Compared with state-of-the-art deep learning-based inpainting methods, our approach provides more than 2dB gain on Places2 dataset with irregular mask.
An Effective Approach on Mining Co-Location Patterns from Spatial Databases with Rare Features

A co-location pattern is a group of spatial features whose instances are frequently appearing together in geography. Co-location pattern mining is particularly valuable for discovering spatial dependencies. Lots of co-location pattern mining approaches have been proposed, but they often emphasize the equal participation of every spatial feature. As a result, the interesting pattern which involves spatial features with significantly different for the number of instances cannot be captured. In this paper, we are committed to address the problem of mining co-location patterns from the spatial database with rare features. Specifically, we first propose a new interest measure, namely the weighted participation index. This interest measure is related to the distribution of the number of instances for spatial features, and it has ability to capture the prevalent co-location patterns with or without rare features. Furthermore, we prove that the weighted participation index possesses the approximate monotonicity property, which can be utilized to improve the computational efficiency, and thereby an efficient algorithm is developed. As demonstrated by extensive experiments, our approach is effective, efficient and scalable for mining co-location patterns embedded in the spatial database with rare features.
A Gaussian Mixture Model Approach to Classifying Response Types

Visual perception is influenced by prior experiences and learned expectations. One example of this is the ability to rapidly resume visual search after an interruption to the stimuli. The occurrence of this phenomenon within an interrupted search task has been referred to as rapid resumption. Previous attempts to quantify individual differences in the extent to which rapid resumption occurs across participants relied on using an operationally defined cutoff criteria to classify response types within the task. This approach is potentially limited in its accuracy and could be improved by turning to data-driven alternatives for classifying response types. In this chapter, I present an alternative approach to classifying participant responses on the interrupted search task by fitting a Gaussian mixture model to response distributions. The parameter estimates obtained from fitting this model can then be used in a naïve Bayesian classifier to allow for probabilistic classification of individual responses. The theoretical basis and practical application of this approach are covered, detailing the use of the Expectation-Maximisation algorithm to estimate the parameters of the Gaussian mixture model as well as applying a naïve classifier to data and interpreting the results.