Reviewerly/exHarmony huggingface dataset:
A Framework for Human-Centric Personalization of Context Recognition Models on Mobile Devices

In context recognition, the recognition model is often built by supervised machine-learning techniques. The recognition performance is likely to be influenced by individual difference, e.g., rhythm of walking, in the target domain. In this paper, we introduce a framework that allows mobile device users to be involved with customization process, in which the key concept is active learning and persuasive technologies. We show a case study with smartphone localization problem, in which we evaluated the effectiveness of active-learning supported by compatibility-based base classifier selection.
Evolving Network Topology in Policy Gradient Reinforcement Learning Algorithms

In this study we examine the influence of incorporating Evolutionary Algorithms (JADE) and Metaheuristic Algorithms (PSO) for evolving neural network topologies in simple policy gradient, A2C and Deep Deterministic Policy Gradient (DDPG) reinforcement learning. A2C is experimented on a discrete action space model free environment followed by DDPG on a continious action model free environment;“LunarlanderContinious-v2” from OpenAI gym. This baseline result is then compared with topology and hyperparameter optimized A2C and DDPG using Differential Evolution and Particle Swarm Optimizaton. The results thus obtained shows that Differential Evolution (JADE) optimized DDPG has the fastest convergence and minimum forgetting of learned weights.
Riemannian Loss for Image Restoration

Deep neural networks are widely used for image restoration, however the loss criteration is usually set as ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> penalizes larger errors, which is unstable for outliers. To avoid the disadvantages, ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> is utilized as a more robust and well behaved loss. This paper proposes a novel loss function for restoration networks, which measures geodesic distance in Riemannian manifold and exploits the outstanding properties of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> . Different from ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> loss which reflects pixel distance, our loss in Riemannian reflects the structure distance of image. The proposed loss not only preserves the robutness of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> loss, but also reflects the image contrasts. Experimental results on image super resolution and compressed sensing show that our proposed loss function achieves more accurate reconstructions, according to both the objective and perceptual qualities.
Mining Customers Opinion on Services and Applications of Mobile Payment Companies in Indonesia Using Sentiment Analysis Approach

The development of technology and digital has also increased the ease of accessing the internet. One aspect of daily life that are affected by the adoption of technology and the internet is the field of payment transactions. Payment transactions are inseparable from everyday life. At this time with the development of technology, payment transactions can be done with the more practical, easy, safe and convenient. The technology is called Financial Technology. Mobile payment is a service that is part of financial technology. The aspects contained in the mobile payment are top up, transfers, cash withdrawals, online payment, and offline payments. Classifications of reviews from Twitter are classified using Support Vector Machine. The results of this study are Go-Pay and OVO must pay attention to every aspect and improve every aspect, of course, to increase customer satisfaction. The accuracy level of the classification model produced for bigram is 92% (Go-Pay) and 93% (OVO). It also shows that sentiment analysis using bigram can improve accuracy level.
Mitigating DoS Attacks against SDN Controller Using Information Hiding

Because of SDN centralization nature, denial of service (DoS) attacks have become a prominent concern. In the OpenFlow (OF) protocol, the transport layer security (TLS) protocol is recommended to secure the control channel. Unfortunately, the tasks involved in the proper configuration of a secured TLS are very challenging. Even worse, TLS is made an optional mode of communication in OF. As a consequence, some OF-enabled switches and controllers do not adopt TLS. In this paper, we develop a lightweight authentication mechanism, called Hidden Authentication (HiAuth), to protect SDN controller against DoS attacks. HiAuth legitimizes SDN forwarding devices by hiding authentication information into the header of control channel packets. Our experimental results prove that HiAuth is lightweight and can not only mitigate DoS attacks, but also provide high undetectability to the attacker.
Adaptive Security of Practical Garbling Schemes

A garbling scheme enables one to garble a circuit C and an input x in a way that C(x) can be evaluated, but nothing else is revealed. Since the first construction by Yao, tremendous practical efficiency improvements for selectively secure garbling schemes –where the adversary is forced to choose both input and circuit to be garbled at the same time– were proposed. However, in the more realistic setting of adaptive security where an adversary can choose the input adaptively based on the garbled circuit little is known about practical efficiency improvements. In this work, we initiate the study of practical garbling schemes that are both more efficient than Yao's construction and adaptively secure. We provide insights into characteristics of these schemes and highlight the limitations of current techniques for proving adaptive security in this regime. Furthermore, we present an adaptively secure garbling scheme that garbles $$\mathsf {XOR}$$ gates with 2 and $$\mathsf {AND}$$ gates with 3 ciphertexts per gate, thus providing the first practical garbling scheme for NC1 circuits with adaptive security based on PRFs whose garbled circuit size is smaller than that of Yao's construction.
Detection and Diagnosis of Psychological Disorders Through Decision Rule Set Formation

Mental illness has long term influences on people's relationships, health, education and profession. According to WHO, psychological disorders are continuously growing its burden for the past few years throwing serious impact on people's health, economic and social condition all over the globe. A similar statistics states that in 2011 about 30 million people worldwide were affected by Major Depressive Disorder, 60 Million by Bipolar Affective and 23 Million by Schizophrenia and other Psychosis [13]. Although medical sciences have come with every effective intervention for the diagnosis and treatment of psychological disorders but still people don't seek out the treatment they need. The major reason for this failure is because of the overlapping symptoms of Psychological disorders. It sometimes takes years by the Psychiatrists to accurately diagnose the psychological disorder. The objective of this research paper is to detect and diagnose more accurately the presence of three of the major and most common psychological disorders i.e. Major Depressive Disorder, Bi-Polar Affective Disorder and Schizophrenia through Decision Rule Set Formation (RST).
Caravel: Burst Tolerant Scheduling for Containerized Stateful Applications

In a containerized environment, the applications are generally categorized as either stateful or stateless, each consisting of multiple containers. Their co-scheduling in a cluster presents unique challenges for the container orchestration frameworks. The two types of applications differ from each other in how they handle the temporary load spikes. The stateless applications can scale out by instantly spawning new identical instances, whereas the stateful applications require deliberate planning to scale, as each application instance is unique. Instead, the stateful applications can more conveniently acquire the additional resources needed during a spike by scaling up on the same node. However, when an application's container uses more than requested resources, it risks being evicted from the node. The evictions are particularly detrimental for stateful applications because of their longer start up time and the resulting degradation. Moreover, the existing container orchestration frameworks schedule or evict containers without any knowledge of its impact on their owning applications. For instance, an eviction of the application's multiple containers in a short period of time could compromise its availability and severely degrade its performance. To address these challenges, we present Caravel, a scheduling approach that provides better experience to stateful applications in dealing with load spikes. It allows them to overstep the resource request during a burst and use the resources on the same node while minimizing their evictions. Moreover, the scheduler provides a fair opportunity to all the stateful applications to use the spare resources in the cluster. The evaluation shows that our approach reduces the eviction of stateful applications by up to 90% over the traditional approach.
An Enhanced Approach Using Collaborative Filtering For Generating Under Graduate Program Recommendations

The Students of class 12 in India choose a program from a pool of programs offered by higher education institutions. It is proven that students end up choosing inappropriate programs as they lack proper guidance in program selection. As there are multiple program options for students after class 12, this research work is towards the recommendation of appropriate programs for the students by using collaborative filtering algorithms. This research work creates an implicit rating matrix using the academic information of the students. The implicit rating matrix is created by using the domain knowledge of the application. Augmenting the domain knowledge with different types of collaborative filtering like user-based and item-based using implicit rating matrix shows an improvement in the performance of the recommender systems.
Progression Modelling for Online and Early Gesture Detection

Online and Early detection of gestures is crucial for building touchless gesture based interfaces. These interfaces should operate on a stream of video frames instead of the complete video and detect the presence of gestures at an earlier stage than post-completion for providing real time user experience. To achieve this, it is important to recognize the progression of the gesture across different stages so that appropriate responses can be triggered on reaching the desired execution stage. To address this, we propose a simple yet effective multi-task learning framework which models the progression of the gesture along with frame level recognition. The proposed framework recognizes the gestures at an early stage with high precision and also achieves state-of-the-art recognition accuracy of 87.8% which is closer to human accuracy of 88.4% on the NVIDIA gesture dataset in the offline configuration and advances the state-of-the-art by more than 4%. We also introduce tightly segmented annotations for the NVIDIA gesture dataset and setup a strong baseline for gesture localization for this dataset. We also evaluate our framework on the Montalbano dataset and report competitive results.
Differentially Private Streaming to Untrusted Edge Servers in Intelligent Transportation System

This paper considers the privacy issues in the intelligent transportation system, in which the data is largely communicated based upon vehicle-to-infrastructure and vehicle-to-vehicle protocols. The sensory data communicated by the vehicles contain sensitive information, such as location and speed, which could violate the driver's privacy if they are leaked with no perturbation. Recent studies suggested mechanisms for randomizing the stream of vehicular data to ensure individuals' privacy. Although the past works on differential privacy provide a strong privacy guarantee, they are limited to applications where communication parties are trusted and/or data is limited to a few types. In this paper, we address this gap by proposing a differentially private mechanism that adds noise in the user side rather than the server. Also, our mechanism is able to perturb various types of data as pointed out by the dedicated short-range communication protocols in the automotive industry. The proposed mechanism is data adaptive and scales the noise with respect to the data type and distribution. Our extensive experiments show the accuracy of our mechanism compared to the recent approaches.
Effective User Preference Clustering in Web Service Applications

Abstract The research on personalized recommendation of Web services plays an important role in the field of Web services technology applications. Fortunately, not all users have completely different service preferences. Due to the same application scenarios and personal interests, some users have the same preferences for certain types of Web services. This paper explores the problem of user clustering in the service environment, grouping users according to their service preferences. It helps service providers to identify and characterize the preferences of similar users and provide them with customized services. We propose two combination-based clustering algorithms which make full use of the advantages of the K-means algorithm and the affinity propagation algorithm. In addition, a three-stage clustering process is elaborated to improve the accuracy of user clustering. To reduce the time complexity of the algorithms, we create a parallel execution model of the algorithms implemented by a higher-order MapReduce sequence linking technology. Extensive experiments on simulated datasets and real datasets are performed on the comparisons between the proposed algorithms and the other combination-based clustering algorithms. The experimental results substantiate that the proposed algorithms can effectively distinguish user group with different preferences.
Theatrical manipulation and seductive sentimentality: constructions of empathy in Swedish online far-right discourse about EU-migrants

Digital technologies such as online forums afford opportunities for users to circumvent and challenge feeling rules. The online far-right uses such opportunities to challenge feeling rules regarding racialised minority groups, refugees and immigrants, including rules about empathy for such groups. However, while the disruptive features of online far-right practice are widely acknowledged, less has been written about how participants in online far-right forums actually make sense of emotional experiences, including empathy in relation to migrants. This article analyses a Swedish far-right-dominated online discussion thread about non-Swedish EU citizens who ask for money, a group referred to in Sweden as ‘EU-migrants’. From an affective-discursive practice perspective, and focusing on metaphorically constructed interpretative repertoires, the article analyses how participants in the thread make sense of empathy as an actual or possible emotional response to these migrants, as well as how the participants position themselves as subjects in relation to such experiences and various actors involved. The results indicate that the participants are formulating a dual affective-discursive position. First, they present themselves as especially capable of empathic resonance and mirroring, but as righteously angered by EU-migrants’ putative attempts to manipulate this empathic capacity. Second, they disidentify with ‘Swedes’ by presenting themselves as capable of avoiding the seductive and sentimental ‘kindness narcomania’ supposedly deeply rooted in Swedish society, and thereby present themselves as a (counter-)empathic vanguard community in relation to this society. The implications of such affective-discursive practices for far-right online and offline action are discussed.
Soil Moisture Prediction using Fuzzy Time Series and Moisture sensor Technology on Shallot Farming

This research conducted by predicting soil moisture using Fuzzy Time Series (FTS) and soil moisture sensor technology on shallot farming. Well-controlled soil moisture affects the shallots and crops growth. It discusses soil moisture prediction and monitoring systems developed through Android-based mobile programming languages. Input data consists of sensor results obtained from automatic, online, and real-time acquisition using soil moisture sensor technology, then, sent to the server and stored in an online database. Furthermore, data acquisition is predicted using the FTS algorithm that applies a discourse universe to define and determine fuzzy sets. Fuzzy set results are continued to the process of sharing the discourse universe so that it becomes the final step. Prediction results are displayed on the information system dashboard developed. Using 24 data from soil moisture data, the predicted score is 760 at the beginning of 6:00. The results of the prediction are done by validating error deviations using the Mean Square Error of 1.5%. This proves that FTS is good enough in predicting soil moisture and safety to control soil moisture in shallots. For deeper analysis, researchers used various request data and U discourse universe at FTS to obtain various results based on the test data used.
Towards Automated Security Vulnerability and Software Defect Localization

Security vulnerabilities and software defects are prevalent in software systems, threatening every aspect of cyberspace. The complexity of modern software makes it hard to secure systems. Security vulnerabilities and software defects become a major target of cyberattacks which can lead to significant consequences. Manual identification of vulnerabilities and defects in software systems is very time-consuming and tedious. Many tools have been designed to help analyze software systems and to discover vulnerabilities and defects. However, these tools tend to miss various types of bugs. The bugs that are not caught by these tools usually include vulnerabilities and defects that are too complicated to find or do not fall inside of an existing rule-set for identification. It was hypothesized that these undiscovered vulnerabilities and defects do not occur randomly, rather, they share certain common characteristics. A methodology was proposed to detect the probability of a bug existing in a code structure. We used a comprehensive experimental evaluation to assess the methodology and report our findings.
Begonia: An Efficient and Secure Content Dissemination Scheme for Smart Cities

Content dissemination, while achieving efficiency and security, is a key challenge in Smart Cities. Existing encryption and key exchange schemes either fail to fulfill the security requirements or incur unaffordable communication, computation, storage and energy overheads. We present a novel approach, Begonia, that provides both efficiency and security when disseminating content to a significantly large number of devices in a smart city context. Begonia makes use of bilinear maps and elliptic-curve cryptography to achieve communication, computation, storage and energy efficiency. This approach completely removes the need for excess communication for key exchange while achieving the desired cryptographic strength. Additionally, it provides dynamic regrouping, joining and isolation capabilities. Experimental results demonstrate huge performance gains when the number of IoT nodes is very large.
A Word2vec Model for Sentiment Analysis of Weibo

The booming of online social media has provided a platform for massive users to share viewpoints and emotional experiences. A huge volume of digital traces that accumulate and aggregate on social media provide a more efficient proxy for investigating users' behaviors, thoughts and emotions. How to precisely and effectively acquire the emotions and topic keywords from these short and colloquial texts is the key task in the analysis of social media. Through neural networks, Word2vec offers a unique contribution to embedding vector construction and expanding similar words. Based on a huge volume of texts on Sina Weibo, the most popular Twitter-like service in China, this paper presents a Word2vec model bringing extra semantic features to fit for short Chinese texts. By comparing with the model based on Internet contents of long texts, the experimental results illustrate that our model can effectively improve the performance of sentiment classification with six categories on Weibo. Furthermore, a series of application results demonstrate the usability and adaptability of our model for massive data on social media.
Towards Personality Classification Through Arabic Handwriting Analysis

Classification of personality types based on Arabic handwriting is a challenging task. It has multiple practical application since handwriting is a unique attribute for each person that provides as much differentiation as fingerprints. Accurate analysis of handwritten documents is useful in diverse areas including human resource management, criminal justice, forensics, security, archaeology, and countless other spheres of life. Writer identification is a big challenge mostly due to a limited human capability for observing and recognizing different styles of writing. Arabic is a language used by millions of people. Recognition of handwritten Arabic characters would, therefore, be of tremendous help in various sectors like mail sorting, verification of checks, etc. even in countries where Arabic is used only occasionally. For this purpose, we collected around 160 samples of Arabic handwriting from 83 different writers, where every writer wrote the same paragraph in both normal and fast writing. Carl Jung’s and Isabel Briggs Myers’ personality type theory with 16 different personality type was used for classifying the writers. The writers were asked to complete the personality test that contains 64 questions related to Jung-Myers’ typology. Specific features of Arabic language handwriting were used to match each written document with a personality type of the writer based on a set of classification techniques. We used four different machine learning classification algorithms with 4-fold cross validation technique. We conducted two experiments and achieved a maximum accuracy of 68.67%.
Autonomous Neurosurgical Instrument Segmentation Using End-To-End Learning

Monitoring surgical instruments is an essential task in computer-assisted interventions and surgical robotics. It is also important for navigation, data analysis, skill assessment and surgical workflow analysis in conventional surgery. However, there are no standard datasets and benchmarks for tool identification in neurosurgery. To this end, we are releasing a novel neurosurgical instrument segmentation dataset called NeuroID for advancing research in the field. Delineating surgical tools from the background requires accurate pixel-wise instrument segmentation. In this paper, we present a comparison between three encoder-decoder approaches to binary segmentation of neurosurgical instruments, where we classify each pixel in the image to be either tool or background. A baseline performance was obtained by using heuristics to combine extracted features. We also extend the analysis to a publicly available robotic instrument segmentation dataset and include its results. The source code for our methods and the neurosurgical instrument dataset will be made publicly available (http://brl.ee.washington.edu/robotics/surgical-robotics/neurosurgical-instrument-segmentation) to facilitate reproducibility.