minOffense: Inter-Agreement Hate Terms for Stable Rules, Concepts, Transitivities, and Lattices

Hate speech classification has become an important problem due to the spread of hate speech on social media platforms. For a given set of Hate Terms lists (HTs-lists) and Hate Speech data (HS-data), it is challenging to understand which hate term contributes the most for hate speech classification. This paper contributes two approaches to quantitatively measure and qualitatively visualise the relationship between co-occurring Hate Terms (HTs). Firstly, we propose an approach for the classification of hate-speech by producing a Severe Hate Terms list (Severe HTs-list) from existing HTs-lists. To achieve our goal, we proposed three metrics (Hatefulness, Relativeness, and Offensiveness) to measure the severity of HTs. These metrics assist to create an Inter-agreement HTs-list, which explains the contribution of an individual hate term toward hate speech classification. Then, we used the Offensiveness metric values of HTs above a proposed threshold minimum Offense (minOffense) to generate a new Severe HTs-list. To evaluate our approach, we used three hate speech datasets and six hate terms lists. Our approach shown an improvement from 0.845 to 0.923 (best) as compared to the baseline. Secondly, we also proposed Stable Hate Rule (SHR) mining to provide ordered co-occurrence of various HTs with minimum Stability (minStab). The SHR mining detects frequently co-occurring HTs to form Stable Hate Rules and Concepts. These rules and concepts are used to visualise the graphs of Transitivities and Lattices formed by HTs.Comment: IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), October 13-16, 2022, Shenzhen, China. IEEE, 2022. (Core A

Design and Fabrication of an Electrical Breakdown Facility

Usage of traditional experimental instrumentation has not kept up with the rate of advancement in the modern educational material. Teaching aids used in academia have to be updated to ensure effective understanding of content among the students. The use of outdated vacuum chambers as visual aids in plasma physics classrooms have proven to be ineffective for the students and teachers, due to limited viewing ports on the metallic walls of the vacuum chamber for viewing the plasma discharge phenomenon. It is important to address this challenge, which invigorates the need for the use of a transparent vacuum chamber as a teaching aid. The design and fabrication of the electrical breakdown facility were a multiple phase project. Firstly, there were various viable solutions designed and analyzed. Secondly, parts were ordered and machined for the required design configuration. Finally, the design was assembled and experiments were conducted for testing and design evaluation. The new vacuum chamber is very efficient in displaying the plasma discharge phenomenon which will enhance the students’ understanding of plasma physics in the classroom. Manufacturing the most effective design is an engineering challenge; of which iterations and analysis of the design throughout the process are an indispensable part, which is why there always a need for additional work in the field

Double Stage chain routing Protocal in WSN

Wireless sensor networking is most popular fields in today’s world. In this paper, we have discussed the different energy optimization protocols of WSN. We have forwarded a new protocol “Double Stage Chain Routing Protocol†from WSN. Our main focus is on extending the residual energy and network’s life time at least more than LEACH, CCM and TSCP. The result of our protocol is represented with the help of graph with comparison with TSCP and it is found that DSCRP gives better network lifetime than LEACH. The proposed algorithm is acceptable in the network lifetime of DSCRP as well as in network life time

Gravity-Magnetic Studies for Uranium Exploration Over Manbazar-Kutni Area of South Purulia Shear Zone (SPSZ), West Bengal, India Using Hydro-Uranium Anomalies as Guidance

Both tectonic belt, the South Purulia Shear Zone(SPSZ) and the Singhbhum Shear Zone (SSZ) within theSinghbhum craton of East Indian Shield, has been identified withsimilar geometrical shape and mineralization. The mineralizationof these regions is mainly structural guided and hydrothermallygenerated. An integrated gravity-magnetic study has beenconducted around Manbazar-Kutni area across SPSZ todecipher the subsurface configurations, presence offaults/fractures. These structural features may form favourablecondition for mineralization. The first degree trend surfaceseparated residual gravity as well as the Bouguer gravity andmagnetic anomaly maps depicted the ESE-WNW trending SPSZon the SW part of the area. The observed negative gravity andmoderately high magnetic anomalies around Dighi, Chepuavillages are also characterized by medium to high hydrouraniumanomaly from earlier hydro-uranium anomaly studies.Therefore, the negative gravity and moderate positive magneticanomaly zones are concluded to be hydrothermally alteredbrecciated zone and the possible uranium mineralized zone. Theinterpreted faults /lineaments from the gravity-magneticanomaly maps show good correlation with the exposed one andwith the hydro-uranium anomalous zones. Further, the 2Dgravity model across the shear zone depicts three low densityaltered zones (most likely sheared granite and mineralizationzone) over the granitic basement along SW-NE profile fromKutni to Chepua village under a thin cover of granitic schist ofCGGC. Since surface signature of nuclear radiation has not beenobserved, uranium mineralized zone could be at a large depthwithin these altered zones. Thus, the study demonstrates theeffectiveness of gravity-magnetic methods in delineatingsubsurface configuration and to identify the alteredzones/faults/lineaments which will act as favourable factors forstructural guided radioactive mineralization in conjunction withother know mineralization indication

A systematic investigation of electric field nonlinearity and field reversal in low pressure capacitive discharges driven by sawtooth-like waveforms

Understanding electron and ion heating phenomenon in capacitively coupled radio-frequency plasma discharges is vital for many plasma processing applications. In this article, using particle-in-cell simulation technique we investigate the collisionless argon discharge excited by temporally asymmetric sawtooth-like waveform. In particular, a systematic study of the electric field nonlinearity and field reversal phenomenon by varying the number of harmonics and its effect on electron and ion heating is performed. The simulation results predict higher harmonics generation and multiple field reversal regions formation with an increasing number of harmonics along with the local charge separation and significant displacement current outside sheath region. The field reversal strength is greater during the expanding phase of the sheath edge in comparison to its collapsing phase causing significant ion cooling. The observed behavior is associated with the electron fluid compression/rarefaction and electron inertia during expanding and collapsing phase respectively

Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering

BACKGROUND: The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell tumor (SRBCT) and thus often leads to misdiagnosis. Identification of biomarkers for distinguishing these cancers is a well studied problem. Existing methods typically evaluate each gene separately and do not take into account the nonlinear interaction between genes and the tools that are used to design the diagnostic prediction system. Consequently, more genes are usually identified as necessary for prediction. We propose a general scheme for finding a small set of biomarkers to design a diagnostic system for accurate classification of the cancer subgroups. We use multilayer networks with online gene selection ability and relational fuzzy clustering to identify a small set of biomarkers for accurate classification of the training and blind test cases of a well studied data set. RESULTS: Our method discerned just seven biomarkers that precisely categorized the four subgroups of cancer both in training and blind samples. For the same problem, others suggested 19–94 genes. These seven biomarkers include three novel genes (NAB2, LSP1 and EHD1 – not identified by others) with distinct class-specific signatures and important role in cancer biology, including cellular proliferation, transendothelial migration and trafficking of MHC class antigens. Interestingly, NAB2 is downregulated in other tumors including Non-Hodgkin lymphoma and Neuroblastoma but we observed moderate to high upregulation in a few cases of Ewing sarcoma and Rabhdomyosarcoma, suggesting that NAB2 might be mutated in these tumors. These genes can discover the subgroups correctly with unsupervised learning, can differentiate non-SRBCT samples and they perform equally well with other machine learning tools including support vector machines. These biomarkers lead to four simple human interpretable rules for the diagnostic task. CONCLUSION: Although the proposed method is tested on a SRBCT data set, it is quite general and can be applied to other cancer data sets. Our scheme takes into account the interaction between genes as well as that between genes and the tool and thus is able find a very small set and can discover novel genes. Our findings suggest the possibility of developing specialized microarray chips or use of real-time qPCR assays or antibody based methods such as ELISA and western blot analysis for an easy and low cost diagnosis of the subgroups