Reviewerly/exHarmony huggingface dataset:
Dying of a hundred good symptoms: why good security can still fail - a literature review and analysis

Many organizations suffer serious information security incidents, despite having taken positive steps towards achieving good security standards. The authors hypothesize that these issues are often as a result of security arrangements not being sufficiently integrated with businesses. We believe that adopting an enterprise architecture (EA) approach to implementing information security – commonly referred to as an 'Enterprise Information Security Architecture' (EISA) – will deliver substantial benefits. Our paper has reviewed and analyzed literature concerning the root causes of information security incidents and describes a novel approach with 8 domains for ensuring critical factors are considered when building an EISA framework.
A Novel Approach for Client Side Encryption in Cloud Computing

Cloud computing has become the latest technological computing area providing a large number of advantage to the different organization with its different business model at low cost. Nevertheless, there is always a security concern when uploading sensitive data in the cloud server. Client-side encryption is a common and better solution for ensuring end users that a third party user cannot access the uploading data. Cloud service providers maintain different techniques to protect data but like google drive, most of the company do not use client- side encryption. In this paper, we propose a way to protect the data from hacking or losing when storing or uploading the data in the cloud server using the combination of Advanced Encryption Standard and Secure Hash Algorithm with Initial Vector.
Optically injected intensity-stable pulse source for secure quantum key distribution

The security of decoy-state quantum key distribution (QKD) highly depends on the accurate control of multiple intensity states. Although several theoretical studies on the QKD with loosely controlled source intensities have been proposed, there is still a large gap between the experimental realization and the theoretical analysis. In this paper, we adopt the gain-switching method to generate short optical pulses, and the corresponding intensity stabilities are quantitatively measured. The method via optical injection is proposed to make effective reductions of the intensity fluctuations from 6.47%∼1.59% to 1.95%∼1.15% at different optical powers. QKD performance adopting the experimental results is also analyzed and discussed. For a typical 40 dB high-attenuation QKD system, the relative increase on the secure key rates reaches 51.89% for the corresponding intensity fluctuations of 1.15% with optical injection and 1.59% without optical injection. The presented intensity-stable optical pulse source can find wide applications in different QKD protocols, such as BB84, DPS, COW, etc.
Exploiting Typicality for Selecting Informative and Anomalous Samples in Videos

In this paper, we present a novel approach to find informative and anomalous samples in videos exploiting the concept of typicality from information theory. In most video analysis tasks, selection of the most informative samples from a huge pool of training data in order to learn a good recognition model is an important problem. Furthermore, it is also useful to reduce the annotation cost as it is time-consuming to annotate unlabeled samples. Typicality is a simple and powerful technique which can be applied to compress the training data to learn a good classification model. In a continuous video clip, an activity shares a strong correlation with its previous activities. We assume that the activity samples that appear in a video form a Markov chain. We explicitly show how typicality can be utilized in this scenario. We compute an atypical score for a sample using typicality and the Markovian property, which can be applied to two challenging vision problems-(a) sample selection for learning activity recognition models, and (b) anomaly detection. In the first case, our approach leads to a significant reduction of manual labeling cost while achieving similar or better recognition performance compared to a model trained with the entire training set. For the latter case, the atypical score has been exploited in identifying anomalous activities in videos where our results demonstrate the effectiveness of the proposed framework over other recent strategies.
Age Prediction based on Brain MRI Images using Extreme Learning Machine

Age Prediction, which means setting up a machine learning system, defined by using different sets of data for training, and then the estimation of the actual age of humans, is a subject that has been studied in recent years. To achieve this, researchers have been experimenting with various body components, such as DNA, speech signals, medical images, facial images, etc. Recent researches show that brain structure changes with age or psychiatric disorders. So a useful tool for estimating the age of humans is the brain's MRI images. Brain Magnetic Resonance Imaging (MRI) use radio waves and a robust magnetic field to create detailed images of the organs and tissues within the body. In this paper, the age of humans is predicted based on brain MRI images. To extract T1-MRI features, two different methods are proposed, then to estimate age, Extreme Learning Machine (ELM) is employed. Given that the amount of computations needed in this method and the time required to age estimation is low, the proposed method has acceptable performance.
Word Sense Disambiguation of Bengali Words using FP-Growth Algorithm

Word Sense Disambiguation (WSD) is the task of determining the specific meaning of an ambiguous word according to the context. In the realm of natural language processing, WSD is an open problem, and its development can significantly assist in human-level machine translation. In this paper, we have proposed a system for Bengali Word Sense Disambiguation through the FP-Growth algorithm. Moreover, we have also implemented the Apriori algorithm and presented an analysis on both of them to explain how the FP-Growth algorithm outperforms the Apriori algorithm performing Bengali Word Sense Disambiguation. Furthermore, In the testing phase we have found that for 80% of the test sentences, our proposed method can retrieve the exact meaning of an ambiguous word.
Efficient runtime metaprogramming services for Java

The Java programming language and platform provide many optimizations to execute statically typed code efficiently. Although Java has gradually incorporated more dynamic features across its versions, it does not provide several metaprogramming features supported by most dynamic languages, such as structural intercession (the ability to dynamically modify the structure of classes) and dynamic code generation. Therefore, we propose a method to add those metaprogramming features to Java in order to increase its runtime adaptiveness, while taking advantage of the robustness of its static type system and the performance of its virtual machine. We support the dynamic addition, deletion and replacement of class methods and fields, and dynamic code generation. The metaprogramming services are provided as a library, so neither the Java language nor its virtual machine are modified. We evaluate our system, called JMPLib, and compare it with the existing metaprogramming systems for the Java platform and other highly optimized dynamic languages. JMPLib obtains similar runtime performance to the existing fastest system that modifies the implementation of the Java virtual machine. Moreover, our system introduces no performance penalty when metaprogramming is not used, and consumes fewer memory resources than the rest of implementations for the Java platform.
Practitioner Evaluations on Software Testing Tools

In software engineering practice, evaluating and selecting the software testing tools that best fit the project at hand is an important and challenging task. In scientific studies of software engineering, practitioner evaluations and beliefs have recently gained interest, and some studies suggest that practitioners find beliefs of peers more credible than empirical evidence. To study how software practitioners evaluate testing tools, we applied online opinion surveys (n=89). We analyzed the reliability of the opinions utilizing Krippendorff's alpha, intra-class correlation coefficient (ICC), and coefficients of variation (CV). Negative binomial regression was used to evaluate the effect of demographics. We find that opinions towards a specific tool can be conflicting. We show how increasing the number of respondents improves the reliability of the estimates measured with ICC. Our results indicate that on average, opinions from seven experts provide a moderate level of reliability. From demographics, we find that technical seniority leads to more negative evaluations. To improve the understanding, robustness, and impact of the findings, we need to conduct further studies by utilizing diverse sources and complementary methods.
Space-variant blur kernel estimation and image deblurring through kernel clustering

This paper presents a space-variant blur kernel estimation and image deblurring framework. For space-variant blur kernel estimation, the input image is divided into small patches, and for each patch, the blur kernel is estimated. The estimated kernels are then grouped to determine different kernel clusters in the image. During clustering, unreliable kernel estimates are eliminated. The blur kernel for each kernel cluster is finally refined using the corresponding image region, which is the union of image patches associated with the kernels in the cluster. For space-variant image deblurring, the entire image is deconvolved with each blur kernel to produce a set of deblurred images. These images are then fused to produce a blur-free image, where the fusion process selects the optimal regions from the set of deblurred images.
Steering Performance with Error-accepting Delays

In steering law tasks, deviating from the path is immediately considered an error operation. However, in navigating a hierarchical menu item, which is a representative application of the law, a deviation within a short duration is sometimes permitted. We tested the validity of the steering law model with various durations of such error-accepting delays and found that it showed high fits for each delay condition (R2 > 0.96) but poor fits if the delay values were not separated (R2 = 0.58). Because the average movement speed linearly increased as the delay increased, we refined the model by taking the delay into account, and the fitness was significantly improved (R2 = 0.97). Our model will help GUI designers estimate the average operational time on the basis of the menu item length, width, and error-accepting delay.
Meteorological Variables’ Influence on Electric Power Generation for Photovoltaic Systems Located at Different Geographical Zones in Mexico

In this study, the relation among different meteorological variables and the electrical power from photovoltaic systems located at different selected places in Mexico were presented. The data was collected from on-site real-time measurements from Mexico City and the State of Sonora. The statistical estimation by the gradient descent method demonstrated that solar radiation, outdoor temperature, wind speed, and daylight hour influenced the electric power generation when it was compared with the real power of each photovoltaic system. According to our results, 97.63% of the estimation results matched the real data for Sonora and 99.66% the results matched for Mexico City, achieving overall errors less than 7% and 2%, respectively. The results showed an acceptable performance since a satisfactory estimation error was achieved for the estimation of photovoltaic power with a high determination coefficient R2.
AJAX Technology for Internet of Things

The IoT device is more and more present in various areas of activity. Interaction with these devices is primarily through web applications that run on both fixed devices and mobile devices. Web applications for IoT devices must provide intuitive dynamic information and allow interaction with them. To build these applications, various technologies are used, including Ajax technology, which enables dynamic data loading.
A Call For Embedding Dignity In Humanitarian Technologies

Increasingly, technologies are being designed to support refugees. While HCI research has explored several aspects of refugee experiences, the importance of embedding the principle of dignity within technological designs is yet to be explored. In this paper we focus on the theme of dignity that was a prominent theme across three research projects we conducted with Syrian refugee communities in Lebanon. We show that the experiences of refugee participants are characterised by a loss of dignity, as well as by attempts to maintain dignity that may be mediated by technology. By highlighting the value given to dignity by our participants we emphasise the need for designers and HCI researchers to consider how technologies may create a space in which dignity is maintained and dignified interaction may take place.
Chapter 12 Virtual Currencies in Modern Societies: Challenges and Opportunities

Virtual currency is a digital representation of value that is neither issued by a central bank or a public authority. Its reliability is based on advanced cryptographic methods which provide privacy and confidence to citizens. Virtual currency and its underlying technologies such as blockchain or smart contracts trigger transformation in many areas of the society’s functioning. The way in which social relations occur and economic transactions are managed are changing forever. As a result, cryptocurrencies constitute a good example of how specific technology may lead to substantial transformation of the world. Still, virtual currencies could benefit from the versatility of collaborative communication of social media and Internet to promote and develop new commerce and business initiatives as well as new forms of financial flow managements. The objective of this chapter is to examine the role played by virtual currencies in modern societies in order to describe potential uses and applications and their impact on politics and social behavior. As a result, recommendations are inferred to address the challenges and opportunities of these new technologies.
VibEye

We present VibEye: a vibration-mediated recognition system of objects for tangible interaction. A user holds an object between two fingers wearing VibEye. VibEye triggers a vibration from one finger, and the vibration that has propagated through the object is sensed at the other finger. This vibration includes information about the object's identity, and we represent it using a spectrogram. Collecting the spectrograms of many objects, we formulate the object recognition problem to a classical classification problem among the images. This simple method, when tested with 20 users, shows 92.5% accuracy for 16 objects of the same shape with various materials. This material-based classifier is also extended to the recognition of everyday objects. Lastly, we demonstrate several tangible applications where VibEye provides the needed functionality while enhancing user experiences. VibEye is particularly effective for recognizing objects made of different materials, which is difficult to distinguish by other means such as light and sound.
Parallel boosted clustering

Scalability of clustering algorithms is a critical issue in real world clustering applications. Usually, data sampling and parallelization are two common ways to address the scalability issue. Despite their wide utilization in a number of clustering algorithms, they suffer from several major drawbacks. For example, most data sampling can often lead to biased solutions due to its inability in accurately capturing the distribution of the entire data set. On the other hand, the performance of parallelization highly depends on the original clustering routines which are not parallel algorithms in nature, such that customizing each algorithm to be parallel may hurt the clustering performance. To alleviate these problems, we propose a general two-step framework for scalable clustering in this work, where the first step is to obtain skeleton structure of data and the second step is to obtain the final clustering. Concretely, data are first partitioned and located across a two-dimensional grid, and then local clustering algorithms are iteratively applied on the cells of the grid, each providing a set of intermediate core points. These core points represent the dense or central regions of data, which can be centers, modes and means for centroid-based, density-based and probability-based clustering, respectively. Finally, these core points are further used to obtain the final clustering. The proposed framework enjoys several benefits: (1) the local clustering on partitioned cells are conducted in parallel and thus can lead to high speed-up; (2) the clustering on the representative core points can be more robust; (3) the framework can be easily applied to other basic clustering methods and thus achieves a general scalable solution. Theoretical analysis is provided and extensive experimental results have demonstrated the effectiveness and efficiency of the proposed framework.
Video Affective Content Analysis by Exploring Domain Knowledge

Film grammar is often used to invoke certain emotional experiences from audiences through changing visual, speech, and musical elements of videos. Such film grammar, referred to as domain knowledge, is of great importance for video affective content analysis but has not been thoroughly examined in research. In this paper, we propose an improved method for emotion recognition and regression from videos through exploring domain knowledge. We first investigate the domain knowledge of visual, speech, and musical elements, and infer probabilistic dependencies between elements and emotions from the summarized film grammar. Then, we transfer the summarized dependencies between elements and emotions as constraints, and formulate video affective content analysis, including both emotion recognition and emotion regression from video content, as a constrained optimization problem. Experiments on the LIRIS-ACCEDE database, the FilmStim database, and the DEAP database demonstrate that the proposed video affective content analysis method can successfully leverage well-established film grammar to improve emotion recognition and regression from video content.
Apprise

Human trafficking and forced labor are global issues affecting millions of people around the world. This paper describes an initiative that we are currently undertaking to understand the role technology can play to support the critical-agency of migrant workers in these situations of severe exploitation. Building on five consultations with more than 170 direct and indirect stakeholders in Thailand, the paper presents the co-design, development, and evaluation of Apprise, a mobile app to support the identification of victims of human trafficking using a Value Sensitive Design approach. It also provides a critical reflection on the use of digital technology in the initial screening of potential victims of human trafficking, to understand in what ways Apprise can support the critical agency of migrant workers in vulnerable situations.
Fusion of histogram based features for Human Action Recognition

Human action recognition is an active research topic which is having real time challenges. Some of the challenges are speed of action, background noise and shape of the performing action. To handle these problem, in this paper the following works are proposed. By the help of optical flow, Bag of Bag of histogram of optical flow (BoHOF) is proposed which is useful to differentiate actions varying with speed of action. BoHOF features are calculated from segmented human objects. To remove the shadow effect, sobel edge filter is used combingly in horizontal and vertical direction. Median filtering is applied to suppress background noise. Histogram of oriented gradients (HOG) features are extracted from 3D projected planes and combined with BoHOF to extract maximum advantages of both the features. Finally, the multi-class SVM-based classifier with radial basis kernel is applied to recognize different human actions. The experiments are conducted on the benchmark KTH dataset and the experimental findings concludes that the proposed HAR technique provides better performance compared to the state-of-the-art techniques.
A novel instrument to compare dynamic object detection algorithms

Nowadays, the amount of dynamic object detection in video sequences algorithms has increased considerably. Notwithstanding the many efforts to provide benchmarking resource, a standard methodology to achieve this evaluation does not exist. Most of the existing benchmarking resources concentrate on the evaluation of the algorithms from a rigid perspective by using just quantitative metric values of the performance. However, these evaluations do not consider important criteria like documentation, auto-adaptability, novelty, speed, which are important factors to consider from a scientific and/or real word application. Therefore, this paper proposes a new methodology to evaluate, compare, and select dynamic object detection algorithms by considering the criteria previously mentioned including performance. The new methodology was developed by analyzing 119 algorithms and the databases CDnet2014, CDnet2012 and BMC. The findings indicate that the proposed methodology preserves consistence with some of the rankings in the databases, but it also provides more complete and useful information in the evaluation of the algorithm.
Cybersecurity for Protection and Control Systems: An Overview of Proven Design Solutions

This article explains practical design principles to follow for networked protection and control (P&C) systems. Proven cybersecurity best practices, system architectures, monitoring methods, and defense-in-depth techniques are described. The risks and proper mitigations are explain ed for many common problems, such as human error, malicious malware, and advanced nation-state zeroday attacks. This article references and summarizes several industry standards.