Miracle-dz/UniFAR-FTUs huggingface dataset:
HealthAssistantBot: A Personal Health Assistant for the Italian Language

In this article, we present HealthAssistantBot, an intelligent virtual assistant able to talk with patients in order to understand their symptomatology, suggest doctors, and monitor treatments and health parameters. In a simple way, by exploiting a natural language-based interaction, the system allows the user to create her health profile, to describe her symptoms, to search for doctors or to simply remember a treatment to follow. Specifically, our methodology exploits machine learning techniques to process users symptoms and to automatically infer her diseases. Next, the information obtained is used by our recommendation algorithm to identify the nearest doctor who can best treat the user’s condition, considering the community data. In the experimental session we evaluated our HealthAssistantBot with both an offline and online evaluation. In the first case, we assessed the performance of our internal components, while in the second one we carried out a study involving 102 subjects who interacted with the conversational agent in a daily use scenario. Results are encouraging and showed the effectiveness of the strategy in supporting the patients in taking care of their health.
Detecting Sugarcane Diseases through Adaptive Deep Learning Models of Convolutional Neural Network

Sugarcane disease causes a threat in the sugarcane industry that leads to massive demolition of infected crops, decreases cultivation and financial loss of farmers. Early detection of the diseases using machine learning technology can avoid such loss and disaster. Subsequently, deep learning is an interesting technology to solve this problem through machine learning. CNN has proven to be a popular technique in recognizing and detecting plant diseases through deep learning. This paper aims to integrate various CNN architectures of deep learning models to achieve the highest accuracy rate in detecting and recognizing sugarcane diseases. The models were trained using 14,725 images of a healthy sugarcane leaves and infested sugarcane diseases and achieves a maximum 95.40% accuracy rate during training. Three architectures of CNN; StridedNet, LeNet, and VGGNet were used in the conduct of this study. VGGNet model tops the other two models achieving an accuracy rate of 95.40%, LeNet model achieves the rate of 93.65%, while StridedNet model achieves 90.10% accuracy rate during training. The three models trained serve its purpose by classifying images of a healthy sugarcane leaves and diseased infested sugarcane leaves based on the leaf features. This paper also bids a successful result for the researchers who are in the development of recognition system for sugarcane diseases.
Bangla Music Genre Classification Using Neural Network

Music genre classification is very vital for music recommendation and for the retrieval of music information. So many works have already been done for classifying genres of English music using different machine learning approaches. Even though Bangla music is very rich in its own fashion, there is almost no notable work found to classify music genres of Bangla music using machine learning techniques yet. There are so many types and styles of Bangla music which can be classified in different genres. Initially, we're considering 6 different Bangla music genres such as ‘Bangla Adhunik’, ‘Bangla Hip-Hop’, ‘Bangla Band Music’, ‘Nazrulgeeti’, ‘Palligeeti’, ‘Rabindra Sangeet’ etc. We are using 250–300 songs (. MP3 files) for each genre. We extracted different time domain and frequency domain features of audio signals from digital audio files (i.e. MP3 files). Finally, in this study, we proposed a deep learning model (after comparing performances of different models) to do a multiclass classification of Bangla music genres.
A Comparison of Concatenated Polar Codes with Different Interleaving and Decoding Schemes

Towards the 5th generation wireless systems (5G) and beyond, polar codes have become a hot research topic in the field of communications. In our work, concatenated polar codes with different interleaving and decoding schemes are designed to improve the error performance of finite-length polar codes. In this paper, concatenated polar codes with outer BCH codes are first constructed, besides, concatenated LDPC codes and concatenated Turbo codes are also designed for comparison. At the same time, random interleaving (RI) scheme and blind interleaving (BI) scheme are proposed to construct the concatenated codes. From the simulation results of this paper, we can see that the bit error rate (BER) performance of concatenated codes using BI scheme is better than that of concatenated codes using RI scheme. Furthermore, the BER performance of concatenated polar codes outperforms that of concatenated LDPC codes, but not as good as that of concatenated Turbo codes. To improve the BER performance of concatenated polar codes, we adopt the CRC Aided Successive Cancellation List (CA-SCL) decoding scheme instead of the Successive Cancellation (SC) decoding scheme for inner polar decoding. The results of our study indicate that the BER performance of concatenated polar codes with CA-SCL outperforms that of concatenated polar codes with SC. In addition to this, with the same CRC code length, increasing the list size can improve the decoding performance of CA-SCL. However, there is also a bad side: the decoding complexity of CA-SCL increases rapidly as the list size increases. On the other hand, with the same list size, increasing the CRC code length adversely deteriorates the decoding performance of CA-SCL.
A Folded Monopole Shaped Novel Soil Moisture and Salinity Sensor for Precision Agriculture Based Chipless RFID Applications

This paper focuses on the design and analysis of an inexpensive novel chipless RFID sensing scheme for soil moisture and salinity content detection. The sensor is designed at the UHF RFID band so that it can be tweaked easily to a chip-based RFID sensor. Alongside the designed sensor, a theoretical soil model with different moisture and salinity levels is simulated here. A fabricated prototype is used to measure the variation of moisture and salinity levels in sandy soil. Both the simulated and measured results exhibit frequency shift of sensor resonance with moisture content variation at non-saline conditions. However, if the soil is saline, the resonance amplitude gets reduced with increased salinity levels and exhibits moisture content independence and hence no frequency variation.
Soft Robotic Module for Sensing and Controlling Contact Force

This work presents a soft robotic module that can sense and control contact forces. The module is composed of a foam spring encapsulated by a pneumatic bellow that can be inflated to increase its stiffness. Optical sensors and a light source are integrated inside the soft pneumatic module. Changes in shape of the module lead to a variation in light reflectivity, which is captured by the optical sensors. These shape measurements are combined with air pressure measurements to predict the contact force through a machine learning model. Using these predictions, a closed-loop control of the contact force was implemented. The modules can be applied to realize pressure distribution control in support devices such as seats and mattresses. The presented method is robust and low-cost, can measure both shape and contact force, and does not require (rigid) sensors to be present at the movable contact interface between the support device and the user.
Revisiting Persistent Hash Table Design for Commercial Non-Volatile Memory

Emerging non-volatile memory technologies bring evolution to storage systems and durable data structures. Among them, a proliferation of researches on persistent hash table employ NVM as the storage layer for both fast access and efficient persistence. Most of them are based on the assumptions that NVM has cacheline access granularity, poor write endurance, DRAM-comparable read latency and much higher write latency. However, a commercial non-volatile memory product, named Intel Optane DC Persistent Memory (AEP), has a few interesting features that are different from previous assumptions, such as 1) block access granularity 2) hardware-layer wear-leveling and 3) much higher read latency than DRAM and DRAM-comparable write latency. Confronted with the new challenges brought by AEP, we propose Rewo-Hash, a novel read-efficient and write-optimized hash table for commercial non-volatile memory. Our design can be summarized into three key points. First, we keep a hash table copy in DRAM as a cached table to speed up search requests. Second, we design a log-free atomic mechanism to support fast writes. Third, we devise an efficient synchronization scheme between persistent table and cached table to mask the data synchronization overhead. We conduct extensive experiments using real NVM platform and the results show that compared with state-of-the-art NVM-Optimized hash tables, Rewo-Hash gains improvement of 1.73x-2.70x and 1.46x-3.11x in read latency and write latency, respectively. Rewo-Hash also outperforms its counterparts by 1.65x-4.24x in throughput for various YCSB workloads.
Optimization Design of Coupling Mechanism of Wireless Power Transfer System Based on LCC / S Topology

In order to optimize the wireless power transfer (WPT) system and design the coupling mechanism reasonably, this paper first analyzes the system efficiency and compensation capacitance voltage characteristics under LCC/S compensation topology, and then explores the shielding principle of ferromagnetic materials and metal materials and the influence on the coupling mechanism parameters. Based on the analysis of the number of turns and the dimension of coils, a design scheme that meets the requirements is designed for the 30kW electric vehicle.
Overcoming Challenges for Achieving High in-situ Training Accuracy with Emerging Memories

Embedded artificial intelligence (AI) prefers the adaptive learning capability when deployed in the field, thus in- situ training on-chip is required. Emerging non-volatile memories (eNVMs) are of great interests serving as analog synapses in deep neural network (DNN) on-chip acceleration due to its multilevel programmability. However, the asymmetry/nonlinearity in the conductance tuning remains a grand challenge for achieving high in-situ training accuracy. In addition, analog-to-digital converter (ADC) at the edge of the memory array introduces an additional challenge - quantization error for in-memory computing. In this work, we gain new insights and overcome these challenges through an algorithm-hardware co-optimization. We incorporate these hardware non-ideal effects into the DNN propagation and weight update steps. We evaluate on a VGG-like network for CIFAR-10 dataset, and we show that the asymmetry of the conductance tuning is no longer a limiting factor of in-situ training accuracy if exploiting adaptive "momentum" in the weight update rule. Even considering ADC quantization error, in-situ training accuracy could approach software baseline. Our results show much relaxed requirements that enable a variety of eNVMs for DNN acceleration on the embedded AI platforms.
Multi-Agents System for Early Disaster Detection, Evacuation and Rescuing

Usually, disasters occurs over a relatively short time at any time and place. Most people are unaware about the prevention or safety awareness to face the disaster. At the time of disaster detection, identifying the place, evacuation and rescuing processes should be timely conducted to save people lives, otherwise losses and consequences are severe. To overcome the consequences accompanied by disasters (e.g. fire, flood and earthquake), this paper proposes a multi-agent system integrated with sensory inputs i.e. fire, flood and collapse detection sensors. An Arduino controller will collect the sensors readings and pass them to agent's system. An intelligent multi-agent system is designed, where eleven agents categorized into four phases to communicate and cooperate together to early detect, evacuate and rescue in case a disaster occurs. The main contributions of the proposed system are: early detection of the disaster before it occurs, determine the safest path for victims to escape and request the help of the emergency and providing its location. The outcome of such proposed system will save many lives and reduce losses.
A Data-Driven Fault Detection Framework Using Mahalanobis Distance Based Dynamic Time Warping

Fault detection module is one of the most important components in modern industrial systems. In this paper, we propose a novel fault detection framework which makes use of both normal and faulty measurement signals at the same time. In this framework, the multivariate time series (MTS) pieces which are extracted from measurement signals in a time interval are used as the training and testing samples, and a $K$ -nearest neighbour rule of MTS pieces is applied for fault detection. Moreover, a Mahalanobis distance based dynamic time warping method is used to measure the divergence among MTS pieces, and a one-class metric learning algorithm is proposed to learn the appropriate Mahalanobis distance. Experimental results on the Tennessee Eastman process demonstrate that the proposed method has improved fault detection performance compared with classical approaches on certain kinds of faults.
Smart Healthcare Device for Cardiac Patients

Heart diseases can be considered as the most occurring long-lasting diseases in modern times. In this paper a standalone smart healthcare device is developed and implemented. The device can monitor the heart condition based on the measured electrocardiogram (ECG) signal and send/save updated report for the patient, doctor or health care center (HCC). This collected data is also processed and analyzed inside the device by using multi-layer perceptron to predict any abnormal behavior. In case of any abnormalities, the device will send an alarm to the patient, and also sort of data could be submitted through the cloud to the health care center. In case of an emergency, the system has the capability to send a call/SMS with location information to the emergency department for an action.
SHPI: Smart Healthcare System for Patients in ICU using IoT

Smart healthcare monitoring systems provide better healthcare service by improving the availability and transparency of health data. However, it also posses serious threats to data security and privacy. As medical internet of things (IoT) are connected to other devices through various networks that provide a suitable attack surface for the intruders. Further, the health data are sensitive, and any breach in security may lead to wrong treatment or compromising the privacy of the patients. In this regard, a secure IoT frame is desirable, which is capable of preserving the integrity and confidentiality of the medical data. In this paper, we have proposed a novel architecture which leverages the blockchain technology to enhance the security and privacy of IoT for healthcare applications. In the proposed architecture called smart healthcare system for patients in ICU (SHPI), critical data is processed in edge computing which is located inside the hospital to reduce the communication latency. In order to provide tramper-proof medical records and data confidentiality SHPI uses blockchain technology and cryptographic methods respectively. Also, a data accessing token system is introduced to separate the group of users based on their roles. This system utilizes smart contracts to record every event for providing transparency in medical activities. In order to describe the working principles a logical analysis is carried out, that shows the system is capable of providing the desired security and privacy.
Efficient Bayesian Exploration for Soft Morphology-Action Co-optimization

Morphology been shown to be a fundamental aspect of tactile sensing in soft robotics, one that can aid, and indeed enable, complex discrimination tasks. For a robot to change its sensor morphology as well as control appropriately, the parametric search over morphology and control parameters is usually slow and unsuited for real-world applications. We develop a framework based on Bayesian Exploration, to allow a robot to co-optimize both changes in tactile sensing morphology and robot action control, to aid in complex tactile object discrimination tasks. We test the framework by performing object discrimination on a set of eight objects, varying three different physical properties: geometry, surface texture, and stiffness. We integrate a capacitive tactile sensor into a flat end-effector and create three soft silicon-based filters with varying morphological properties. We incorporate the end-effector onto a robotic arm and perform repetitive, parameterized touch experiments, on each object. We show morphing is indeed necessary to dissociate amongst different object properties with the sensor at hand. Moreover, we show the proposed framework can consistently achieve optimal morphology-action configurations in approximately half the time than systematic search over parameters. This work marks a step towards the creation of robots capable of using morphology and action control to actively aid in discrimination tasks.
Anomaly-Based Intrusion Detection From Network Flow Features Using Variational Autoencoder

The rapid increase in network traffic has recently led to the importance of flow-based intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly-based methods, which can identify unknown attacks are also integrated into these systems. In this study, the focus is concentrated on the detection of anomalous network traffic (or intrusions) from flow-based data using unsupervised deep learning methods with semi-supervised learning approach. More specifically, Autoencoder and Variational Autoencoder methods were employed to identify unknown attacks using flow features. In the experiments carried out, the flow-based features extracted out of network traffic data, including typical and different types of attacks, were used. The Receiver Operating Characteristics (ROC) and the area under ROC curve, resulting from these methods were calculated and compared with One-Class Support Vector Machine. The ROC curves were examined in detail to analyze the performance of the methods in various threshold values. The experimental results show that Variational Autoencoder performs, for the most part, better than Autoencoder and One-Class Support Vector Machine.
COSECHAR: Genomic Coding Sequence Characterization for Heterologous Host Expression

Synthetic biology involves the creation of genomic sequences to design biological systems with specific purposes. However, the synthesized sequences have to function within a particular cellular environment, and in some cases they fail to express. Phenomena such as mutation, cell death, and interactions with cellular context could be influenced by the particular characteristics of organisms, such as the organism’s codon and codon pair bias, as well as the presence restriction sites or splice sites in the nucleotide sequences. Several measures to analyze heterologous expression have been proposed. Here we present a web based tool, COSECHAR, available at http://bio.cs. miami.edu/COSECHAR/. The tool analyses a sequence provided by the user, using quantifiable measures, and it presents information relevant to the expression of the sequence in a species selected by the user. The tool returns nucleotide counts, codon and codon pair usage scores. In addition, the tool retrieves data from external databases and publicly available tools to identify restriction sites and splice sites. COSECHAR handles several types of input from the user. The sequence can be pasted, or uploaded in a file. Additionally, an accession number or GI number can be entered and the sequence is automatically retrieved from NCBI’s websites. The host organism can be specified to obtain the codon and codon pair bias information. On the other hand, the restriction site and splice site information is retrieved from external tools that use predefined organisms. In addition, COSECHAR features a help section, where more information about the individual measures and external tools can be obtained. In summary, COSECHAR is a web based tool that provides a centralized hub to analyze a sequence and identify patterns or characteristics relevant to a species.
Learning to Automate the Design Updates From Observed Engineering Changes in the Chip Development Cycle

The behavioral revisions to the design are frequent in the late stage of the semiconductor chip development. Quite often, their realization emphasizes incrementality that seeks minimum perturbation of the existing implementation. This tutorial paper poses the engineering change order (ECO) problem as the functional decomposition and proposes its solution in the form of the Boolean equations. We hope that the sufficient generality of the statement will be useful in extending the existing stateof-the-art design revision techniques. To assist in this process, we present an observed variety of design revisions encountered in the chip development cycle. The knowledge of such frequent and realistic ECOs is essential in advancing a tool’s ability to yield compact implementation updates. We believe that sharing our experience of practical ECOs would benefit the research community in developing an open-source tool.
Frequent Access Pattern-based Prefetching Inside of Solid-State Drives

This paper proposes an SSD-inside data prefetching scheme, which has features of OS-dependence and use transparency. To be specific, it first mines frequent block access patterns that reflect the correlation among the occurred requests. Then it compares the requests in the current time window with the identified patterns, to direct fetching data in advance. Furthermore, to maximize the cache use efficiency, we construct a method to adaptively determine the cache partition on the basis of I/O workload characteristics, for separately buffering the prefetched data and the write data. Experimental results demonstrate that our proposal can yield improvements on average read latency by 6.3% to 9.3% without noticeably increasing write latency, in contrast to conventional SSD-inside prefetching schemes.
Exact DAG-Aware Rewriting

We present a generic resynthesis framework for optimizing Boolean networks parameterized with a multi-level logic representation, a cut-computation algorithm, and a resynthesis algorithm. The framework allows us to realize powerful optimization algorithms in a plug-and-play fashion. We show the framework’s versatility by composing an exact DAG-aware rewriting engine. Disjoint-support decomposition and SAT-based exact synthesis together with efficient caching strategies enable the algorithm to resynthesize larger parts of the logic. DAGaware rewriting is used to compute the gain of resynthesis while taking the benefit of structural hashing into account.