Reviewerly/exHarmony huggingface dataset:
Model For Mitigating Smishing Attacks On Mobile Platforms

Cybercrime has increased as a result of technology improvements and people&#x0027;s increasing reliance on smartphones and other technologies. SMS enables the dissemination of critical information, which is especially significant for non-digital savvy users who are typically the most isolated. Smishing, often known as SMS phishing, is the practice of sending phony text messages to trick someone into divulging personal information or installing malware. In Kenya, smishing crimes have been noted to escalate at a higher rate. However, there is no comprehensive investigation that has been done involving smishing attacks. There are various proposed solutions for mitigating smishing attacks. However, no existing solution authenticates the sender, filters the smishing content from the message, and informs the user of the potentially harmful content. Therefore, this study presents a novel method to authenticate the sender, filter smishing content from the message, and informs the user in case potentially harmful content exists in the message. Python, MYSQL, and Na&#x00EF;ve Bayes classifier were used to develop the model. The model will help mobile phone users to identify fraudulent messages sent by smishers quickly and effectively.
AIDA-DB: A Data Management Architecture for the Edge and Cloud Continuum

There is an increasing demand for stateful edge computing for both complex Virtual Network Functions (VNFs) and application services in emerging 5G networks. Managing a mutable persistent state in the edge does however bring new architectural, performance, and dependability challenges. Not only it has to be integrated with existing cloud-based systems, but also cope with both operational and analytical workloads and be compatible with a variety of SQL and NoSQL database management systems. We address these challenges with AIDA-DB, a polyglot data management architecture for the edge and cloud continuum. It leverages recent development in distributed transaction processing for a reliable mutable state in operational workloads, with a flexible synchronization mechanism for efficient data collection in cloud-based analytical workloads.
Exploring the necessary conditions for observing interleaved practice benefits in math learning

Previous research has demonstrated benefits of interleaved practice over blocked practice for learning mathematical formulas. This experiment tested whether the benefits from interleaved practice would generalize to more complex problems, where the problem type must be inferred from information in the problem. We compared delayed test performance of participants assigned to blocked practice to participants assigned to interleaved practice who had high or low practice performance. University students (Mage = 18.97, SDage = 1.50, 64% female) learned how to solve probability word problems in blocked practice, interleaved practice, or hybrid conditions that included both kinds of practice. Conditions that included some interleaved practice outperformed a condition that included only blocked practice at delayed test. Participants with high performance on interleaved practice problems outperformed participants assigned to blocked practice at delayed test. These results suggest that interleaved practice can confer learning advantages even for more complex problems.
Model Extraction Attack and Defense on Deep Generative Models

Abstract The security issues of machine learning have aroused much attention and model extraction attack is one of them. The definition of model extraction attack is that an adversary can collect data through query access to a victim model and train a substitute model with it in order to steal the functionality of the target model. At present, most of the related work has focused on the research of model extraction attack against discriminative models while this paper pays attention to deep generative models. First, considering the difference of an adversary` goals, the attacks are taxonomized into two different types: accuracy extraction attack and fidelity extraction attack and the effect is evaluated by 1-NN accuracy. Attacks among three main types of deep generative models and the influence of the number of queries are also researched. Finally, this paper studies different defensive techniques to safeguard the models according to their architectures.
Sentiment Analysis of Citation Sentences using Machine Learning Techniques

The scientific citations, sentiment analysis got very much attention in the latest period as there is an increase in the scientific publications availability. Scholarly databases and publications are the valuable sources for citation information where authors will write about other paper ideas and results by contradicting or verify them. Citation sentence can be an essential indicator in finding the relevancy and importance of an article for the author based on various aspects of the cited work such as corpus, technique, task, method, measure, concept, tool, methodology, and model etc., Citation Sentiment Analysis is a technique to classify the sentiment polarity of citations in scientific articles. The Citation sentences corpus bears three polarities viz. positive, negative, and neutral. It is used to find the contribution towards the research field of the author and to find limitations and identifying problems in a specific approach through negative citation sentences. This will help the upcoming researchers who are searching for the research topic or research articles to focus on. We used Word2Vec model to get the word vectors and applied classification algorithms like SVM, Random forest, and linear SVC on the feature vectors and extracted the sentiment. By using the accuracy, recall, precision, and F-measure metrics we analyzed the performance of each algorithm in the classification of sentiment from the scientific citations.
Automated training of location-specific edge models for traffic counting

Deep neural networks are the state of the art for various machine learning problems dealing with large amounts of rich sensor data. It is often desirable to evaluate these models on edge devices instead of relying on cloud computing. In this paper, we perform traffic counting using surveillance cameras. Edge computing is required as only aggregated counts should leave the device and not the privacy sensitive video frames. Unfortunately, only small object detection models are suited for edge devices which results in sub-optimal performance. We introduce location specific models that are each trained for one specific camera. The model does not need to generalize to other locations. We show that smaller specialized models can outperform large general purpose models. We propose an automated way to train these small models without human intervention. We experimentally show that we can achieve a similar counting accuracy with 5x fewer parameters than state-of-the-art techniques.
Privacy preserving collaborative learning of generalized linear mixed model

Generalized Linear Mixed Model is one of the most pervasive class of statistical models. It is widely used in the medical domain. Training such models in a collaborative setting often entails privacy risks. Standard privacy preserving mechanisms such as differential privacy can be used to mitigate the privacy risk during training the model. However, experimental evidence suggests that adding differential privacy to the training of the model can cause significant utility loss which makes the model impractical for real world usage. Therefore, it becomes clear that the specific class of generalized linear mixed models which lose their usability under differential privacy requires a different approach for privacy preserving model training. In this work, we propose a value-blind training method in a collaborative setting for generalized linear mixed models. In our proposed training method, the central server optimizes model parameters for a generalized linear mixed model without ever getting access to the raw training data or intermediate computation values. Intermediate computation values that are shared by the collaborating parties with the central server are encrypted using homomorphic encryption. Experimentation on multiple datasets suggests that the model trained by our proposed method achieves very low error rate while preserving privacy. To the best of our knowledge, this is the first work that performs a systematic privacy analysis of generalized linear mixed model training in collaborative setting.
Vibe: An Implicit Two-Factor Authentication using Vibration Signals

The increased need for online account security and the prominence of smartphones in today’s society has led to smartphone-based two-factor authentication schemes, in which the second factor is a code received on the user’s smartphone. Evolving two-factor authentication mechanisms suggest using the proximity of the user’s devices as the second authentication factor, avoiding the inconvenience of user-device interaction. These mechanisms often use low-range communication technologies or the similarities of devices’ environments to prove devices’ proximity and user authenticity. However, such mechanisms are vulnerable to colocated adversaries. This paper proposes Vibe-an implicit two-factor authentication mechanism, which uses a vibration communication channel to prove users’ authenticity in a secure and non-intrusive manner. Vibe’s design provides security at the physical layer, reducing the attack surface to the physical surface shared between devices. As a result, it protects users’ security even in the presence of co-located adversaries-the primary drawback of the existing systems. We prototyped Vibe and assessed its performance using commodity hardware in different environments. Our results show an equal error rate of 0.0175 with an end-to-end authentication latency of approximately 3.86 seconds.
Multiple Views Based Recognition of Human Activities using Uniform Patterns

Great efforts are made for the recognition of a person&#x2019;s activity, still it is challenging research domain in security and surveillance. This paper proposes an efficient framework to recognize activities captured from multiple views by incorporating cameras placed at different viewing angle. These viewpoints may be horizontal, vertical, top-down as well as several others. Sometimes three cameras are placed for this purpose whereas sometimes number of cameras may be five or eight. The framework includes 3 consecutive modules that are: to locate humans in a video using background subtraction method, to extract uniform LBP and to classify human actions/activities using SVM multiclass classifier with OVA architecture. The rotation invariant characteristic of LBP supports in human activity classification from multiple views. In addition to this, better discrimination capability of these patterns provides high efficiency to the proposed framework. A hierarchical classification technique has been implemented and multiple SVMs are aggregated to classify human activities. Experimentation was performed on CASIA and IXMAS activity datasets and demonstrates the effectiveness of the proposed framework for multiple views.
Word Expansion using Synonyms in Indonesian Short Essay Auto Scoring

Exams conducted in online learning to evaluate learning processes have many formats, including essay format. Essays are considered more proper to measure learning activity results. However, essays require longer to assess student answers and have consistency problems if the assessment is carried out by different teachers or done separately. This study investigates the influence of word expansion using synonyms in Indonesian thesaurus on short essay auto scoring. The first step, reference answers and student answer text data is preprocessed by case folding, stemming, stop word removal, tokenizing, and duplicate word removal. Second, Word expansion using synonyms in thesaurus is used to generate alternate words for reference answers. Third step, the scoring process by calculating similarity and matching words. The score from the similarity and matching results is then used to generate the final score. Performance evaluation shows that the Dice Coefficient similarity method achieved the highest correlation by a very good correlation, and the smallest MAE was achieved by the Cosine Coefficient similarity method.
Hue-preserving image enhancement via complementary enhancing terms

Hue-preserving image enhancement is a very challenging and important research topic in image/video processing and computer vision fields. In the past decades, spectral methods, histogram-based methods, and spatial methods are proposed to partially tackle this problem. However, most of them still suffer from the challenges in enhancing color images while preventing hue shift. In this paper, we build two complementary terms in a variational framework for enhancing images: one is for increasing the image brightness, and the other is for strengthening the edge structure. The two enhancing terms are adaptively united to enhance color images and preserve the original hue simultaneously. The existence and uniqueness of minimizer for the proposed variational model are rigorously established. An efficient numerical implementation of our model is also accomplished via the popular split Bregman method. A number of numerical experiments and comparisons with some state-of-the-art methods are conducted to demonstrate the good performance of the newly proposed model.
Enabling video conferencing in low bandwidth

Frequent disruption in network connectivity is a major challenge in offering good quality of experience to the users of smart mobile applications. Various apps like Zoom, Google Meet, Skype, etc. for online video calling have become indispensable overnight due to this new paradigm shift in home-bound remote work culture driven by the Covid19 pandemic. However, the performance of these apps is tightly bound to the current network conditions. Under limited network coverage and low bandwidth, the data frames suffer from delay, jitter, and loss resulting in degraded quality of experience for the users. In this paper, we propose a server-less peer-to-peer architecture for the video conferencing apps with in-built adaptive compression techniques. The proposed architecture enables video streaming at a very low data rate just to offer a smooth streaming experience under poor connectivity. Video is compressed by ASCII encoding on the basis of the contrast factor of each pixel in the frame. It has been observed that only 29 KBPS bandwidth is sufficient to conduct video conferencing.
Sentiment Analysis of Big Cities on The Island of Java in Indonesia from Twitter Data as A Recommender System

Text mining is a data mining technique to find hidden things from a set of data in the form of text. One of the things that can be obtained with text mining is opinion or sentiment, whether it is positive or negative. Positive sentiment is used as a reference for a subject or object from a collection of texts to be recommended. Java Island is the city with the most population in Indonesia with a variety of culinary delights. This study aims to analyze sentiment recommendations on culinary data from food or cuisine in big cities on the island of Java. Four big cities were selected, namely Jakarta, Bandung, Yogyakarta, and Surabaya. The data source is a tweet of culinary arts of the four cities from Twitter in Indonesia. The Sastrawi Library is used as a text mining data processing tool in Indonesia. The results obtained are the majority of positive sentiments from all cities. The cumulative sentiment value of the four cities is 54%, meaning that the big cities on the island of Java have good culinary delights and deserve to be recommended to be enjoyed.
Location Independent Gesture Recognition Using Channel State Information

Gesture recognition has been the subject of intensive research in recent years owing to its wide applications. Unlike traditional systems, which usually require wearable sensors, many recent works have achieved the desirable gesture recognition performance using wireless channel state information from commercially available WiFi devices. However, existing works generally require training new models for different locations due to the location-dependent nature of channel state information. This paper proposes a location-independent system that can recognize gestures performed in a new location without training a new model. Our approach uses disentanglement that extricates location and other extraneous information from those needed for gesture recognition. The implementation is based on an unsupervised invariance induction framework consisting of feature extraction, a multi-output latent space, gesture recognition, and decoder modules. The key idea in designing this system is to separate gesture-dependent features from location-dependent features. Specifically, the feature extraction module consisting of a long short-term memory network is employed to select representative features; it essentially serves as an encoder to generate the latent space. During the training process, the network learns to cluster features representation for the gesture recognition and decoder by minimizing the total loss of the gesture recognition and decoder modules. We test our system with a dataset collected from various subjects performing four different gestures in multiple locations in seven rooms with different layouts. The results show that our location-independent gesture recognition system can achieve 88.69% accuracy for new locations.
Evaluation of Impact of Intermediate GPS Spoofing to Mobile Terminals

GPS is known to be vulnerable to GPS spoofing. This can be divided into three stages based on technical aspects: Simplistic, Intermediate, and Sophisticated [1]. Intermediate spoofing is a feasible and threatening technology. However, most of the existing research on mobile device spoofing focuses only on the Simplistic spoofing, which is the least threatening, and there are few experimental-based studies specifically on Intermediate spoofing. In this article, we conduct an experiment of Simplistic and Intermediate spoofing on mobile terminals using an open-source GPS signal generation simulator and analyze the threats.
Bandung Rainfall Forecast and Its Relationship with Niño 3.4 Using Nonlinear Autoregressive Exogenous Neural Network

The city of Bandung, as the capital city of West Java, is one of several areas in Indonesia with high rainfall. This situation can cause disasters, such as floods and landslides, that can harm many parties. Rainfall in Indonesia, particularly on the island of Java itself, is closely related to the global phenomenon of Niño 3.4. In the period from January 2001–November 2021, the rainfall and Niño 3.4 showed some extreme values. In order to foresee the disasters, an accurate rainfall forecast should be performed. For this reason, we try to construct a model of rainfall forecast and its relation to the global phenomenon of Niño 3.4 using the nonlinear autoregressive exogenous neural network (NARX NN). The result shows that NARX NN (13-7-1) with a Mean Absolute Percentage Error (MAPE) value of 6.26% and R2 of 85.37% is best suited for the prediction of this phenomenon. In addition, this study provides forecast results for the next six periods, which can be used as a reference for the relevant authorities to foresee the possibility of flooding in Bandung city. From the forecast results, it can be concluded that the highest rainfall forecasts in the city of Bandung are in February 2022, and will slowly decrease in March 2022. To prevent hydro-meteorological disasters, such as floods in Bandung city, the community can clear waterways, such as clogged drains, rivers, and dams, as well as prepare tools for evacuation.
The information library infrastructure of Uzbekistan: The three-level management model

The current trends in information infrastructure and its component information library infrastructure (ILI) are analyzed. The specific characteristics of Uzbekistan ILI are: building libraries for modern technological applications and developing existing infrastructure. The main problems the ministries and library community face are revealed. The ILI organizational model based on hierarchy is discussed. The author considers the three-level system to be the most efficient for the libraries’ interdepartmental interaction. This would provide for information cooperation in building union resources (e-catalogs, databases), and library management. Taking into account the specific character of library management in the Republic of Uzbekistan, the ILI based on the corporate library networks of various departments would enable: to computerize information acquisition, accumulation and transfer at all levels for efficient management; to provide open access to union e-catalog for all libraries and users; to build libraries’ local digital resources; to increase efficiency of interaction and cooperation between the National Library and the libraries of various departments; to save money and human assets through excluded work duplication and intensive information exchange.
Implementing Existing Authentication Models In USSD Channel

Unstructured Supplementary service data (USSD), a capability built-in Global System for Mobile Communication (GSM) mobile phones, is one of the technologies that make use of the direct PIN entry method. Sadly, the Direct PIN entry method is susceptible to shoulder surfing attacks. USSD technology is text-based, as a result, supports only authentication methods that are in plain text. As a result of this, existing user authentication models against shoulder surfing cannot be implemented on USSD technology. The user authentication model is an area of research interest as a result of the high level of insecurity associated with the proliferation of mobile-based services. The user authentication model has gained more research attention in recent years. Various authentication models against shoulder surfing attacks have been proposed, adopted, and implemented in recent years, yet the authentication model against shoulder surfing attacks that can be implemented in the USSD channel remains an area of research interest. Many existing Authentication models against Human shoulder surfing attacks are applications that use graphical, textual, and biometric (primary and soft) techniques that are based on, image display, image capturing, colors, and so on. In this paper, an attempt has been made to identify why a direct PIN entry method that is prone to attack is still implemented in the USSD channel while there are many strong authentication models against shoulder surfing attacks in existence. To do this, some existing authentication models against shoulder surfing attacks were analyzed and their model requirements identified.