Reviewerly/exHarmony huggingface dataset:
Untyped lambda calculus with functionally referable environments

The environment is the relationship between variables and their bound values during program execution and is a notion in program semantics. A first-class environment is a mechanism that allows the environment to be treated like data, such as integer values or Boolean values, and can be passed to a function as an argument or received as a return value. The environment calculus is a formal computational system proposed by Nishizaki and is a lambda calculus that extends the first-class environment mechanism. The formulation of the environment was based on explicit substitution by Curien et al., who viewed the environment as a substitution. The operational semantics of the environmental calculus, or the reduction, is based on the reduction of the lambda-sigma calculus. In the calculus, there are two constructs for first-class environments: one is the identity environment to reify the current environment, that is, to transfer a meta-level environment to object-level data; the other is the environment composition to reflect the object-level environment data, that is, to transfer object-level environment data back to a meta-level environment. In this paper, instead of the environment composition, we propose a new interface with a first-class environment, a functionally referable environment. If object-level environment data is given as an argument for a function application, the functional reflection brings the environment back to the meta-level and makes the lambda term evaluable under that environment. Using the functionally referable environment, one can unify the environment composition with the function application. We define the untyped lambda calculus with functionally referable environments: we give the syntax of the calculus and its reduction. Then we provide the semantics for the reduction using a translation of the environment calculus into the record calculus. We prove the soundness of the translation semantics. Finally, we discuss the evaluation strategy, especially the call-by-value reduction.
Autonomous Seismic Sensor with a New Temporal Method of a Moving Person Detection

The article presents an autonomous seismic sensor developed to detect a moving person using temporal characteristics of a seismic signal. In order to exclude false alarms associated with moving animals, an additional temporal informative classification feature of the seismic signals from a moving person was introduced into a person detection algorithm, in contrast to known seismic sensors. This feature is associated with a difference in a movement nature of humans and animals. Sensor tests have shown that it has a significant probability of the correct person detection along with low power consumption. It is shown that such characteristics can be achieved using a relatively simple temporal method of the moving person detection. The range of use of the developed sensor is the protection of various objects, border and remote monitoring of the people movement.
Affine OneMax

A new class of test functions for black box optimization is introduced. Affine OneMax (AOM) functions are defined as compositions of OneMax and invertible affine maps on bit vectors. The black box complexity of the class is upper bounded by a polynomial of large degree in the dimension. Tunable complexity is achieved by expressing invertible linear maps as finite products of transvections. Finally, experimental results are given to illustrate the performance of search algorithms on AOM functions.
E-SEEK - RFID based Detection System for Localizing the Misplaced Objects

This paper demonstrates how the E-Seek model assists in locating misplaced objects for industrial and domestic places. Using ultra high frequency passive RFID Tags and readers help in location tracking of assets is done. The random real time inputs generated using python and it's graphical interfaces, RawTurtle via Tkinter helps in the simulation of the distance and direction of the tags.
Approaches for Multilingual Phone Recognition in Code-switched and Non-code-switched Scenarios Using Indian Languages

In this study, we evaluate and compare two different approaches for multilingual phone recognition in code-switched and non-code-switched scenarios. First approach is a front-end Language Identification (LID)-switched to a monolingual phone recognizer (LID-Mono), trained individually on each of the languages present in multilingual dataset. In the second approach, a common multilingual phone-set derived from the International Phonetic Alphabet (IPA) transcription of the multilingual dataset is used to develop a Multilingual Phone Recognition System (Multi-PRS). The bilingual code-switching experiments are conducted using Kannada and Urdu languages. In the first approach, LID is performed using the state-of-the-art i-vectors. Both monolingual and multilingual phone recognition systems are trained using Deep Neural Networks. The performance of LID-Mono and Multi-PRS approaches are compared and analysed in detail. It is found that the performance of Multi-PRS approach is superior compared to more conventional LID-Mono approach in both code-switched and non-code-switched scenarios. For code-switched speech, the effect of length of segments (that are used to perform LID) on the performance of LID-Mono system is studied by varying the window size from 500 ms to 5.0 s, and full utterance. The LID-Mono approach heavily depends on the accuracy of the LID system and the LID errors cannot be recovered. But, the Multi-PRS system by virtue of not having to do a front-end LID switching and designed based on the common multilingual phone-set derived from several languages, is not constrained by the accuracy of the LID system, and hence performs effectively on code-switched and non-code-switched speech, offering low Phone Error Rates than the LID-Mono system.
Conceptual Design and Analysis of a Mobile Digital Identity for eHealth Applications

As mobile technology continues to improve, more and more professional services are being offered as mobile apps. This paradigm shift also affects eHealth applications. Digital identities in nation-wide eHealth infrastructures are often realized via smart cards, which however, do not support mobile applications well. In this paper we propose a concept of a mobile eID for eHealth based on smartphones with embedded secure hardware, an mobile authenticator app and an account manager as well as an Identity Provider (IdP) as backend services. The practical applicability of the concept is shown using the example of the German eHealth infrastructure. Our method generates a cryptographic key pair in secure hardware on the user’s smartphone, registers it on the IdP and uses it to authenticate on the IdP. The security of the private key and the integrity of the smartphone is also validated and attestated. The user’s established smartcard-based identity "Electronic Health Card" (EHC) forms the trust anchor. To authenticate against specialist eHealth apps the IdP issues standard-compliant OAuth2.0/OIDC tokens with a limited period of validity. Furthermore, in our security analysis we demonstrate that based on specific security requirements for smartphones and operating systems, at least the eIDAS security level "substantial" related to the technical security aspects of the system can be achieved. On the basis of this research German legislation was adjusted and "digital identities" supplementary to the smartcard-based EHC will be issued from 2023 in the German eHealth infrastructure.
Identification of drone thermal signature by convolutional neural network

This paper presents the work on drone detection and identification using thermal infrared emission, which is primarily aimed towards night operation. Through both indoor and outdoor trials, the characteristics of the thermal signature emitted by a drone when captured by a drone detection system is examined, and their implications on a machine learning problem are studied. Thermal maps are processed through a YOLOv3 based CNN model to detect and generate a bounding box around the thermal signature of the drone. The presented approach also seeks to utilise the characteristics of drone motion for more effective drone detection through machine learning.
Trusted and Collaborative Data Sharing with Quality Awareness in Autonomous Driving

Autonomous vehicles (AVs) are coming with great potentials to bring safer, greener, and more convenient transportation systems. As AVs rely on radar, camera, and other advanced sensors to sense its surroundings, a salient challenge of AVs is the intrinsic limitations of onboard sensors (e.g., limited awareness range, blind spots, and failure in foggy days). To tackle this problem, we propose a collaborative data sharing scheme for AVs to make up for sensor deficiencies by promoting sensory information sharing in autonomous driving. However, this brings another fundamental issue on how to ensure trust in shared sensory data from distrustful collaborators and how to motivate AVs to participate in data sharing. This work studies this issue by modeling it as a quality-aware optimal sensing task scheduling problem. Specifically, we design an edge computing-enabled architecture where AVs can form collaborative sensing groups in executing sensing tasks. After that, a quality-aware auction-based incentive mechanism is developed to promote AVs’ participation and high-quality data sharing. We also design a reputation model to recruit trustworthy AVs to perform sensing tasks based on their behaviors and social identities. Due to the NP-hardness of problem, we devise a heuristic algorithm to determine the optimal winners and payments in auction with truthfulness and individual rationality guarantees. Lastly, extensive simulations validate that our approach can effectively improve sensing data quality and user utility, compared with conventional schemes.
Mobiles for Development – A Comparative Analysis of Business Decisions

Much research on 'mobiles for development' focuses on outcomes and impact of services. This paper focuses on the underlying business models. It draws on two case studies as part of a UK Aid funded evaluation of the GSM Association led mNutrition programme. Vodafone Farmers Club in Ghana provides agricultural and nutrition information via SMS and Wazazi Nipendeni in Tanzania is a maternal and child health SMS service. The paper presents the salient points of the two contrasting business models, highlighting the strengths and weaknesses of both. The merits of the different approaches depend on the principle motivation, but when it comes to delivering positive outcomes among the poorest, the weight of evidence appears to be in favor of an independent, third party driven model.
Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment

Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.
User embedding-based approach for detecting group shilling attacks

Group shilling attacks pose a serious challenge to the credibility of recommender systems. To improve the robustness of recommender system, a user embedding-based method is proposed to detect group shilling attacks. First, a user-item interaction graph is built by mining the interactive relationships between users and items in the recommender system and the user vector representations are generated using the Struc2Vec algorithm. Second, the set of suspicious target items is obtained through analyzing the centralization of ratings of items. Finally, the suspicious degrees of users rating the suspicious target items are calculated and the attack groups are obtained with a clustering method. Experiments on the Amazon dataset and Netflix dataset show that the proposed method has better detection performance than other methods.
Design and Development of a Smart Wheelchair

Differently abled is a complex phenomenon that reflects an interaction between the features of a person”s body and the features of the society in which he or she lives. Such persons are seen as being restricted in performing daily activities because of a complex set of interrelating factors, some pertaining to the person, and some to the environment and social/political arrangements. There are many disabilities that make a person differently-abled. Hearing loss, Visual disability, Psychiatric disorders, Mobility, and Physical Impairments, Spinal Cord Disability, Brain Disability, etc. Mobility and Physical Impairments make people suffering from partial paralysis or quadriplegia which affect their limbs. One of the best options for such patients is an appropriate wheelchair which can be controlled by any one of the moving parts of the body. The idea of a smart wheelchair with the help of computers, sensors, and supportive technology is developed. The proposed wheelchair detects the head movement and generates the signals with the help of a headgear with a Gyro sensor. The tilting of the head in left, right, forward and backward is identified by the sensors and the signals are transferred and converted this to the motion of the wheel with the help of a motor that is powered by a battery. This wheelchair avoids the use of joysticks and controls and at the same time provides an economical and easy solution for bringing independence into the life of a quadriplegia patient. The safety of the wheelchair is being taken into account while designing the wheelchair.
A suggested optical setup for duplicated-image transport intensity equation technique to accurate determine the optical anisotropy in fibers

In this paper, an optical setup is suggested to simultaneously record a duplicated-image transport intensity equation technique (TIE) for an anisotropic material. The suggested method is characterized by its ability to simultaneously determine the refractive indices of anisotropic fibers at the different polarization directions using only single set captured micrographs. The advantage of suggested optical setup, for determining the birefringence, is minimizing the number of captured micrographs used in computations compared to the traditional TIE technique. The optical anisotropy of the High-density polyethylene (HDPE) fiber was determined using the traditional and the suggested method to verify our suggested optical setup. The suggested method is used to determine the refractive indices and birefringence profiles of highly oriented Poly (ethylene terephthalate) fiber (PET), partially oriented poly (ether ether ketone) fiber (PEEK) and medium anisotropic polyamide-6 fibers. The obtained results are compared with the published data for these fibers and found to be in good agreement.
Exploring Children User Experience in Designing Educational Mobile Application

The use of mobile application facilitates an interactive and innovative learning experience for students. While there are many studies emphasizing on the use of mobile application but less focuses on the student's experience in using those applications. Mobile application designers tend focus on user interfaces but neglects user experience (UX) as their approach in designing educational application for children. Thus, this paper aims to present a model called EduMobile UX model, a user experience model that designers can adopt when designing educational applications for children. To develop the model, an extensive literature was performed to determine the dimensions for the model. The elements for each dimension of the model was identified through semi-structured interviews and observation respondents from a public school in Tronoh, Perak. This study contributes to the development of a UX model that designers can use to develop education mobile applications based on UX principles..
Discomfort Monitoring System using IoT applied to a Wheelchair

A wide variety of wheelchairs are already available in the market. However, discomfort monitoring is also not a standard feature for it. There are several types of discomfort that wheelchair users experience. This study mainly focuses on feelings of distress, such as wetness discomfort due to human wastes like urine. It also focuses on the uneven distribution of pressure on the surface, resulting in pressure sores and monitoring the user's stress through heart rate analysis and skin conductance. The discomfort monitoring system uses ECG, GSR, Wetness, and Pressure Sensors. With the ReLU activation function, the design used a neural network to predict the discomfort level felt by the user. IoT applications in the system include user detection, an LED indicator for the discomfort level, SMS alerts, and the execution of emergency calls. Based on the results, all the features extracted from the four sensors exhibited correlation to the discomfort felt by the user. The most correlated parameter to the discomfort level is from the ECG, next is pressure, followed by wetness, and lastly, GSR.
Robust Real Time Multiple Human Detection and Tracking for Automatic Visual Surveillance System

Detection of human for visual surveillance system provides most important rule for advancement in the design of future automation systems. Human detection and tracking are important for future automatic visual surveillance system (AVSS). In this paper we have proposed a flexible technique for proper human detection and tracking for the design of AVSS. We used graph cut for segment human as a foreground image by eliminating background, extract some feature points by using HOG, SVM classifier for proper classification and finally we used particle filter for tracking those of detected human. Our system can easily detect and track humans in poor lightening conditions, color, size, shape, and clothing due to the use of HOG feature descriptor and particle filter. We use graph cut based segmentation technique, therefore our system can handle occlusion at about 88%. Due to the use of HOG to extract features our system can properly work in indoor as well as outdoor environments with 97.61% automatic human detection and 92% automatic human detection and tracking accuracy of multiple human
Path Planning for Mobile Robot in 3D Environment Based on Ant Colony Algorithm

Abstract This paper introduces the ant colony algorithm to the path planning of mobile robots, and explores the optimal path solution. The definition of path planning was studied firstly. Then modelling method of path planning was discussed. Also, current research status of three-dimensional path planning at home and abroad he existing problems of 3D path planning was analyzed. These basic theoretical knowledge have laid the foundation for the research work of this paper, and we then modelled the three-dimensional terrain environment, and used MATLAB software for simulation experiments to implement the ant colony algorithm to plan the robot’s three-dimensional terrain path, and finally performed fuzzy reinforcement learning method based on ant colony algorithm to find the optimal path for the mobile robot.
Intelligent fall detection system using traditional and non-traditional machine learning algorithm based on MQTT

The population of elderly people exposed to the risk of fall increases each year as reported by World Health Organization (WHO). Fall detection system presented normally is high cost, large size and not efficient. Wearable-based sensor fall detection system developed in this project which were small size, portable and low-cost. The concept of Message Queuing Telemetry Transport (MQTT) applied in this fall detection system to ease the process of data transmission from motion sensor to Raspberry Pi for classification via Wi-Fi. A small size and lightweight microcontroller (Wemos D1 mini ESP 8266) integrated with MPU6050 motion sensor to sense and publish the motion data. Raspberry Pi 3 Model B applied to carry out classification of the motion data. Machine learning algorithms used for classification in comparison were k-Nearest Neighbors (k-NN) and Long Short-Term Memory (LSTM) of Recurrent Neural Network (RNN). LSTM achieved better result at 97% than k-NN at 94%. Smartphone used to publish the notification via an application known as Blynk.
Heavy Minerals from Gopalpur Coast, Odisha with Special Reference to Ilmenite: Implication to Provenance

The intertidal zone as well as sand dunes of Gopalpur coast, Odisha contains heavy minerals of varying proportions ranging from 6.51 to 8.45 wt% out of > 63 µm fractions. The heavy mineral assemblages are ilmenite, magnetite, garnet, sillimanite, zircon, rutile and hornblende of which ilmenite is dominant. Electron probe micro analysis of ilmenite grain indicates that it contains TiO2 in the range of 50.72% to 58.96% and FeO 37.4% to 57.15% in addition to Cr2O3 (0 to 0.12%), NiO (0 to 0.27%), MnO (0.06% to 2.04%) and MgO (0.17% to 2.16%). Mn/Mg ratios show lower values (0.13 to 2.76) for most of the samples, indicating basic rock source such as pyroxene granulites. Few ilmenite grains are having higher Mn/Mg values varying between 4.55 and 6.58, might have been derived from charnockite and khondalites in the hinterland. Scanning electron microscope photographs of ilmenites show sub-rounded to rounded grains with few grains having angular to sub-angular feature. Their micro morphological features indicate effect of mechanical and chemical weathering. Flattened striated surface with grooves feature may be due to mechanical weathering. Etch marks and pits may be due to differential chemical action in low energy sub-aqueous environment of deposition.