Miracle-dz/UniFAR-FTUs huggingface dataset:
Preserving Privacy in Mobile Health Systems Using Non-Interactive Zero-Knowledge Proof and Blockchain

The advent of miniaturized mobile devices with wireless communication capability and integrated with biosensors has revolutionized healthcare systems. The devices can be used by individuals as wearable accessories to collect health data regularly. This type of medical assistance supported by mobile devices to monitor patients and offer health services remotely is known as mobile health (mHealth). Although mHealth provides many benefits and has become popular, it can pose severe privacy risks. Many features in mHealth are managed through a smartphone. Thus, one of the most worrying issues involves communication between the monitoring devices and the smartphone. When communication uses Bluetooth, it is standard for a device to be paired with the smartphone; but generally, it is not exclusively associated with a specific mHealth app. This characteristic can allow a data theft attack by a malicious app or fake data injection by an illegitimate device. To address this issue, we present an authentication scheme based on Non-Interactive Zero-Knowledge Proof that is lightweight enough to run on mHealth devices with minimal resources. Our scheme ensures that legitimate devices interact exclusively with the official mHealth application. To ensure the patient’s privacy-preserving throughout the system, we address the issues of storing, managing, and sharing data using blockchain. Since there is no privacy in the standard blockchain, we present a scheme in which the health data transmitted, stored, or shared are protected by Attribute-Based Encryption. The outcome is a system with fine-grained access control, entirely managed by the patient, and an end-to-end privacy guarantee.
JAK1 palmitoylation by ZDHHC3/7 is Essential for Neuropoietic Cytokine Signaling and DRG Neuron Survival

Janus Kinase-1 (JAK1) plays key roles in pro-survival signaling during neurodevelopment and in responses to neuronal injury. JAK1 was identified as a potential palmitoyl-protein in high-throughput studies, but the importance of palmitoylation for JAK1’s roles in neurons has not been addressed. Here, we report that JAK1 is endogenously palmitoylated in cultured Dorsal Root Ganglion (DRG) neurons and, using an shRNA knockdown/rescue approach, reveal that JAK1 palmitoylation is important for neuropoietic cytokine-dependent signaling and neuronal survival. We further identify the related palmitoyl acyltransferases (PATs) ZDHHC3 and ZDHHC7 as dominant regulators of JAK1 palmitoylation in transfected non-neuronal cells and endogenously in neurons. At the molecular level, palmitoylation is critical for JAK1’s kinase activity in transfected cells and even in vitro, likely because palmitoylation facilitates transphosphorylation of key sites in the JAK1 activation loop. These findings provide new insights into palmitoylation-dependent control of neuronal development and survival.
ABLE: Attention Based Learning for Enzyme Classification

Classifying proteins into their respective enzyme class is an interesting question for researchers for a variety of reasons. The open source Protein Data Bank (PDB) contains more than 1,60,000 structures, with more being added everyday. This paper proposes an attention-based bidirectional-LSTM model (ABLE) trained on oversampled data generated by SMOTE to analyse and classify a protein into one of the six enzyme classes or a negative class using only the primary structure of the protein described as a string by the FASTA sequence as an input. We achieve the highest F1-score of 0.834 using our proposed model on a dataset of proteins from the PDB. We baseline our model against seventeen other machine learning and deep learning models, including CNN, LSTM, BILSTM and GRU. We perform extensive experimentation and statistical testing to corroborate our results.
Implementation of a Wireless Sensor Network Designed to Be Embedded in Reinforced Concrete

This paper presents the McBIM project (Material communicating with the Building Information Modelling) which aspires to provide a communicating reinforced concretes, thus, addresses the design and an implementation of a wireless sensor network devoted to be embedded in reinforced concrete in order to assure structural health monitoring tasks during its entire lifetime. This wireless sensor network is composed of communicating nodes -collecting, processing and sharing with other communicating materials and with virtual models the generated data, and wirelessly powering and controlling the sensing nodes- and sensing nodes -measuring their internal or environmental parameters, pre-processing and transmitting these to the communicating nodes-. The sensing nodes are battery-free and wirelessly powered by a far-field (radiative) wireless power transmission system. Experiments in the air and from a reinforced concrete beam provide encouraging results.
A Novel Clothing Attribute Representation Network-Based Self-Attention Mechanism

As highly increasing of on-line fashions retail industry, automatic recognition and representation of clothing items have huge potentials. With the help of deep learning methods, many clothing attribute representation models have been proposed. However, these models are mainly suitable for coarse-grained classification which are not suitable for clothing attribute representation. To address such a problem, in this article, we propose a novel network structure named SAC, which is a combination of CNNs and Self-attention mechanism and can represent clothing attributes more fine-grained. Besides, we use Grad-CAM to visualize which part of the clothing attributes is more concerned by customers. Finally, a new labeled clothing dataset is introduced in this article, which is expected to be helpful to the researchers who are working in fashion domains for image representation.
Multiphase fault tolerant distributed control techniques for integrated drives based on resonant regulators

One of the challenges brought forward by the gradual electrification undertaken by the aviation sector is the requirement of fault tolerance for machine drive systems to be used for critical on-board tasks such as propulsion or primary flight surface actuation. Their inherent advantages in both volumetric and gravimetric power density makes integrated drives the prime candidates for these applications. Despite the large advances in this field, few key area still need work. Key among which is fault tolerant current control strategies. This paper studies the application of resonant control techniques to achieve a scalable and fault tolerant current control strategy for multiphase machine.
A History and Theory of Textual Event Detection and Recognition

There is large and growing amounts of textual data that contains information about human activities. Mining interesting knowledge from this textual data is a challenging task because it consists of unstructured or semistructured text that are written in natural language. In the field of artificial intelligence, event-oriented techniques are helpful in addressing this problem, where information retrieval (IR), information extraction (IE) and graph methods (GMs) are three of the most important paradigms in supporting event-oriented processing. In recent years, due to information explosions, textual event detection and recognition have received extensive research attention and achieved great success. Many surveys have been conducted to retrospectively assess the development of event detection. However, until now, all of these surveys have focused on only a single aspect of IR, IE or GMs. There is no research that provides a complete introduction or a comparison of IR, IE, and GMs. In this article, a survey about these techniques is provided from a broader perspective, and a convenient and comprehensive comparison of these techniques is given. The hallmark of this article is that it is the first survey that combines IR, IE and GMs in a single frame and will therefore benefit researchers by acting as a reference in this field.
Machine Learning for Financial Risk Management: A Survey

Financial risk management avoids losses and maximizes profits, and hence is vital to most businesses. As the task relies heavily on information-driven decision making, machine learning is a promising source for new methods and technologies. In recent years, we have seen increasing adoption of machine learning methods for various risk management tasks. Machine-learning researchers, however, often struggle to navigate the vast and complex domain knowledge and the fast-evolving literature. This paper fills this gap, by providing a systematic survey of the rapidly growing literature of machine learning research for financial risk management. The contributions of the paper are four-folds: First, we present a taxonomy of financial-risk-management tasks and connect them with relevant machine learning methods. Secondly, we highlight significant publications in the past decade. Thirdly, we identify major challenges being faced by researchers in this area. And finally, we point out emerging trends and promising research directions.
Computation of Worst-case Operation Scenarios against False Data Injection Attacks Considering Load Demand and Generation Uncertainties

False data injection attack (FDI) is one of the most malicious cyber attacks in power systems. However, uncertainties of power systems are neglected in existing works. Focusing on this issue, a bilevel optimization approach to model the worst-case operation of power systems against FDI attacks is proposed, where the attacker is in the upper level to maximize power flow of target line and the control center is in the lower level to adjust generation outputs according to received measurement data. In this model, uncertainties of renewable generations and load demands are mainly considered in the upper level. Then the worst-case operation scenario is determined by transforming the model into a single mixed-integer linear programming problem based on Karush-Kuhn-Tucker (KKT) conditions. The numerical simulations on the modified IEEE 14bus system show that coordination of fluctuations and FDI attacks has more serious effects on the power flow of a target line than only fluctuations or FDI attacks. It is also found that reducing proportion of renewable generations and increasing proportion of fluctuated loads make lines overloaded more easily. Our analysis provides an meaningful insight on the effects of FDI attacks when uncertainties are considered.
Dual-Band Wireless Power Transmitter with Reconfigurable Power Amplifier and "Decoupling Ring"

There are 2 wireless power standards for the mobile device market: Qi standard and AirFuel standard with frequencies of hundreds of kHz and 6.78 MHz. This paper presents a dual-band wireless transmitter operating at both 200 kHz and 6.78 MHz. A dual-band reconfigurable PA with Class E and half-bridge modes is introduced to decrease the size of transmitter. Meanwhile, the eddy current loss model of parallel connected dual-band coils is established to provide guidance for coil design. Finally, a dual-band charging pad with specially designed "decoupling ring" is proposed, which can effectively reduce the extra loss of dual-band operation.
New insights on the morphology and occurrence of the Palaeogene dinoflagellate cyst species Duosphaeridium rugosum and on the taxonomic affinity of the genus Duosphaeridium

The extinct organic-walled dinoflagellate cyst Duosphaeridium rugosum was first described from the Paleocene (Danian) of the southern USA. It has since been documented at several other localities around the world. Well-preserved specimens from the Danian of the Dakhla Formation in the Dababiya Corehole in southern Egypt, illustrated by light and scanning electron microscope images, reveal new morphological details, most notably parasutural features revealing the paratabulation. Detailed observations indicate that Duosphaeridium rugosum has an L-type ventral arrangement, an essentially straight sulcus, and apparently dextral torsion indicating a tentative affinity with the gonyaulacacean subfamily Cribroperidinioideae. Duosphaeridium rugosum occurs in an assemblage with many Danian markers; such as Danea californica, Carpatella cornuta and Lanternosphaeridium reinhardtii. Its earliest or first occurrence is contemporaneous with the upper part of nannoplankton zone NP2 and the uppermost part of the planktonic foraminiferal Parasubbotina pseudobulloides (P1a) zone. Its first occurrence is thus a useful stratigraphical marker for the Danian (earliest Paleocene). Duosphaeridium rugosum is associated with relatively abundant dinoflagellate cyst taxa such as Spiniferites, Achomosphaera, Fibrocysta, Pterodinium, Cordosphaeridium and Exochosphaeridium, which characterize neritic marine settings.
Characterization Research on I/O Improvements Targeting DISC and HPC Applications

Improvements in I/O architectures are becoming increasingly required nowadays. This is an essential point to complex and data intensive scalable applications. Data-Intensive Scalable Computing (DISC) and High-Performance Computing (HPC) applications frequently need to transfer data between storage resources. In the scientific and industrial fields, the storage component is a key element, because usually those applications employ a huge amount of data. Therefore, the performance of these applications commonly depends on some factors related to time spent in execution of the I/O operations. However, researchers, through their works, are proposing different approaches targeting improvements on the storage layer, thus, reducing the gap between processing and storage. Some solutions combine different hardware technologies to achieve high performance, while others develop solutions on the software layer. This paper aims to present a characterization model for classifying research works on I/O performance improvements for large scale computing facilities. Analysis over 36 different scenarios using a synthetic I/O benchmark demonstrates how the latency parameter behaves when performing different I/O operations using distinct storage technologies and approaches.
The influence of urbanisation on aquatic mollusc fauna: the Krzna River (east Poland)

The mollusc fauna of a medium-sized lowland river Krzna, which is almost entirely regulated and affected by urbanisation, was surveyed. The aim of the study was to determine if negative effects of urbanisation were evident in mollusc communities: their species richness, diversity and abundance, as well as composition and dominance patterns. Thirty one living mollusc species and empty shells of another two species were recorded. The simplification of the dominance pattern observed especially within and below Łuków and within Międzyrzec Podlaski may point to a negative effect of urbanisation. A decrease in species richness and diversity in response to urbanisation was recorded within Międzyrzec Podlaski, as well as below Łuków and Biała Podlaska. The mean abundance showed an increase in two urban and two downstream stretches. The relatively ambiguous response of malacocoenoses to urbanisation may result from the elimination of sensitive mollusc species by stressors associated with regulation of the river and release of untreated or insufficiently treated sewage in the past decades. Both the regulation of the river and urbanisation may have contributed to the negative effects on the malacocoenoses, but separation of the influence of these two anthropogenic stressors may be very difficult. kEy words: mollusc assemblages, diversity, urban river
Game-Theoretic Approach to Self-Regulation of Dynamic Network Infrastructure to Protect Against Cyber Attacks

The paper presents the concept of applying a game theory approach in infrastructure of wireless dynamic networks to counter computer attacks. The applying of this approach will allow to create mechanism for adaptive reconfiguration of network structure in the context of implementation various types of computer attacks and to provide continuous operation of network even in conditions of destructive information impacts.
Probabilistic Assessment of the VSC-HVDC Contribution in Voltage Stability Applied to a Hybrid DC-Multi-Infeed Scenario

The still-growing penetration of HVDC links interfaced by Line Commutated Converters (LCC) raises questions about how their operational characteristics will affect the system reliability, particularly in situations of converters stations nearby. In those cases, interaction phenomena tend to reduce voltage and system stability, mainly concurrent commutation failure in the inverters stations caused by near faults in the AC side, which may lead to unwanted interruption of operation. Therefore, techniques to mitigate adverse interactions need to be studied. In this context, this paper presents a commutation failure mitigation technique analysis, in which a VSC-HVDC provides reactive support and direct enhancement in voltage stability in a multi-infeed with two LCC-HVDC links. Besides, this work also presents a statistical study between two methods of VSC-HVDC current control strategies and their efficiency in mitigating commutation failure.
Viscoelastic Response Ultrasound: Methods, Validation, and In Vivo Clinical Applications of a New Approach to Viscoelastic Property Assessment

Viscoelastic Response (VisR) ultrasound is a noninvasive method for interrogating the viscoelastic properties of tissue by observing, in the region of excitation, tissue displacement in response to successive acoustic radiation force impulses. Recent VisR technology advancements now enable estimation of mechanical anisotropy and texture as well as, via machine learning approaches, quantitative evaluation of elastic and viscous moduli (as in Quantitative VisR (QVisR)). The diagnostic relevance of VisR outcome measures have been demonstrated for monitoring renal transplant status, differentiating malignant from benign breast masses, and delineating dystrophic muscle degeneration. For the latter two applications, method and pilot clinical results are reviewed herein. VisR-derived relative elasticity (RE) and relative viscosity (RV) mechanical anisotropy measures differentiated malignant from benign breast masses in women with sensitivities of 0.94 and specificities of 1.00, with accuracies of 0.96. In dystrophic muscle, QVisR-derived longitudinal shear elastic modulus in rectus femoris, vastus lateralus, sartorius, and gastrocnemius muscles statistically different between dystrophic and control for ages less than six years (Wilcoxon, p<0.05). Overall, the body of work describing VisR methods and their applications suggests that VisR is a pertinent tool for clinical tissue viscoelasticity assessment.
Lost in translation: the pitfalls of Ensembl Gene annotations between human genome assemblies and their impact on diagnostics

Background The GRCh37 human genome assembly is still widely used in genomics despite the fact an updated human genome assembly (GRCh38) has been available for many years. A particular issue with relevant ramifications for clinical genetics currently is the case of the GRCh37 Ensembl gene annotations which has been archived, and thus not updated, since 2013. These Ensembl GRCh37 gene annotations are just as ubiquitous as the former assembly and are the default gene models used and preferred by the majority of genomic projects internationally. In this study, we highlight the issue of genes with discrepant annotations, that have been recognized as protein coding in the new but not the old assembly. These genes are ignored by all genomic resources that still rely on the archived and outdated gene annotations. Moreover, the majority if not all of these discrepant genes (DGs) are automatically discarded and ignored by all variant prioritization tools that rely on the GRCh37 Ensembl gene annotations. Methods We performed bioinformatics analysis identifying Ensembl genes with discrepant annotations between the two most recent human genome assemblies, hg37, hg38, respectively. Clinical and phenotype gene curations have been obtained and compared for this gene set. Furthermore, matching RefSeq transcripts have also been collated and analyzed. Results We found hundreds of genes (N=267) that were reclassified as “protein-coding” in the new hg38 assembly. Notably, 169 of these genes also had a discrepant HGNC gene symbol between the two assemblies. Most genes had RefSeq matches (N=199/267) including all the genes with defined phenotypes in Ensembl genes GRCh38 assembly (N=10). However, many protein-coding genes remain missing from the current known RefSeq gene models (N=68) Conclusion We found many clinically relevant genes in this group of neglected genes and we anticipate that many more will be found relevant in the future. For these genes, the inaccurate label of “non-protein-coding” hinders the possibility of identifying any causal sequence variants that overlap them. In addition, Important additional annotations such as evolutionary constraint metrics are also not calculated for these genes for the same reason, further relegating them into oblivion.
Molecular evidence indicates the existence of multiple lineages of Sperata species in Indian Rivers

Sperata seenghala (Giant river-catfish) and Sperata aor (Long-whiskered catfish) are commercially important freshwater catfishes of India, belongs to family Bagridae. Due to high nutritional significance and the low number of intramuscular bones, both fishes have considerable demand in South Asian countries. Both of the Sperata species are morphologically close and well adapted to the same habitat. In this study, we have assessed the level of genetic diversity and differentiation of S. seenghala and S. aor in the Ganga River based on the mitochondrial DNA (mtDNA) control region and compared with the other major Indian rivers. We found high haplotypes diversity for both the species in the Ganga. However, it was comparatively low for S. seenghala in Mahanadi and Brahmaputra populations. The phylogenetic and median-joining network strongly indicated the presence of two distinct maternal lineages of S. seenghala from the Ganga river. Interestingly, the genetic differentiation between S. seenghala of Ganga-Brahmaputra was much higher (~25.3%) than the S. seenghala and S. aor (~17%), whereas it was comparatively low between Ganges-Mahanadi (~8.0%). Our finding provided evidence that all the three rivers: Ganga, Mahanadi, and the Brahmaputra sustain a highly diverse and genetically distinct stock of giant river catfish; therefore, all populations should be considered as a different management unit for the protection of stocks. Our findings indicated that Brahmaputra lineages qualify the species level variations. This study can be further used as a reference database for proper lineage identification of S. seenghala and S. aor that could formulate the appropriate conservation and management plans.